System and method for scanning a region using a low discrepancy curve

ABSTRACT

A scanning system and method for scanning for an object within a region, or for locating a point within a region. Embodiments of the invention include a method for scanning for an object within a region using a Low Discrepancy Curve (LDC) scanning scheme. The method may: 1) generate a Low Discrepancy Sequence (LDS) of points in the region; 2) calculate an LDC in the region based on the LDS of points; and 3) scan the region along the LDC to determine one or more characteristics of the object in response to the scan. In calculating the LDC in the region based on the LDS of points, the method may connect sequential pairs of the LDS with contiguous orthogonal line segments (each parallel to a respective axis of the region), then sample the segments, generating points which may be used to generate the LDC, such as by a curve fit.

PRIORITY CLAIM

[0001] This application claims benefit of priority of U.S. provisionalapplication Serial No. 60/266,658 titled “System and Method for Scanninga Region” filed Feb. 5, 2001, whose inventors were Lothar Wenzel, RamRajagopal, Dinesh Nair, Joseph Ting and Sundeep Chandhoke.

FIELD OF THE INVENTION

[0002] The present invention relates to the field of data acquisition,and more particularly to scanning a region. Description of the RelatedArt Many scientific and engineering tasks involve scanning a region,such as an image or object, to acquire data characterizing the region.Examples of such tasks include parts inspection for automatedmanufacturing systems, alignment tasks for automated assembly systems,and recognition and location tasks in machine vision and motion controlsystems, among others.

[0003] In a typical scanning system a computer system is coupled to acamera which is operable to acquire optical or image information from atarget object. The computer system may take any of various forms. Thesystem may also include hardware and corresponding software which isoperable to move one or both of the camera and the target object toperform the scan.

[0004] In robotics and motion planning an understanding of theunderlying geometry of space is important. Various techniques have beendeveloped to scan regions under various constraints or toward specificgoals. In many cases the geometry is known in advance and a specificgoal is desired. Examples of such goals include:

[0005] (a) Travel in the shortest time from a given point A of a spaceto another point B of the space.

[0006] (b) Travel the shortest path from a given point A of a space toanother point B of the space.

[0007] (c) Travel a path through a space such that any point in thespace is within a specified distance of the path. In other words, thespace or geometry must be scanned completely or almost completely.

[0008] (d) Same as (c) but the path or trajectory may go outside of theoriginally given space.

[0009] (e) All the above cases but with curvature constraints added.

[0010] A more complicated situation may occur when the underlyinggeometry of the space is unknown and a scanning strategy must be appliedto explore the structure of the space.

[0011] The tasks (a) and (b) are well-understood and classical designtools (optimal control) are available. Tasks (c) and (d) belong to aclass of coverage path planning problems where a path must be determinedguaranteeing that an agent will pass over every point in a givenenvironment. Typical applications include, but are not limited to:mine-countermeasure missions, continental shelf oceanographic mapping,contamination cleanup, floor scrubbing, crop plowing, non-destructivetesting, and bridge inspections, among others. Most coverage plannersare still based on heuristics and the smoothness of the developedtrajectories is rarely an issue.

[0012] Choset and Pignon (See Howie Choset, Philippe Pignon, CoveragePath Planning: The Boustrophedon Cellular Decomposition) have developeda boustrophedon decomposition that generalizes the concept of exactcellular decomposition and which results in a union of non-intersectingregions composing the target geometry. The coverage path planningproblem is solved in elementary cells and the derived sub-paths areconcatenated appropriately. The resulting schemes are essentially basedon combinations of back-and-forth motions, i.e., lines are the buildingblocks of these strategies.

[0013] There are many other coverage algorithms, as well. In almost allcases the goal is to guide a robot or sensor to explore or to act withinan environment. See, for example, J. Colgrave, A. Branch, “A Case Studyof Autonomous Household Vacuum Cleaner”, AIAA/NASA CIRFFSS, 1994. Seealso M. Ollis, A. Stentz, “First Results in Vision-Based Crop LineTracking”, IEEE International Conference on Robotics and Automation,1996.

[0014] One promising method in motion planning is based on Morsefunctions. These procedures look at the critical points of a Morsefunction to denote the topological changes in a given space. See, forexample, Howie Choset, Ercan Acar, Alfred A. Rizzi, Jonathan Luntz,“Exact Cellular Decompositions in Terms of Critical Points of MorseFunctions”. See also Ercan U. Acar, Howie Choset, “Critical PointSensing in Unknown Environments”. However, Morse functions find theirprimary use with regard to the scanning of un-smooth surfaces, and soare not generally useful for many applications.

[0015] FIGS. 1A-D—Prior Art Scanning Paths

[0016] FIGS. 1A-D illustrate various scanning paths, according to theprior art. It should be noted that the physical characteristics of ascanning apparatus may place restrictions on what scanning paths may besuitable for a given application. As described below, various prior artscanning schemes may be appropriate for particular applications, but maynot be generally applicable due to high curvatures and/or severestart/stop requirements.

[0017]FIG. 1A—Peano Curve Space-filling Path

[0018] Scanning given geometric objects in two- or higher-dimensionalspaces is a well-studied topic in the mathematical literature.Space-filling curves or umbrella surfaces have often been regarded aspathological objects. Indeed, such structures are generallyinappropriate for real scanning scenarios. Although space-filling isachieved in a very mathematical sense, the curves themselves areunrealizable from a motion control standpoint as they are extremelyirregular. FIG. 1A illustrates a space-filling path known as a PeanoCurve. As may be seen, although the Peano Curve fills the space, thecomplexity and extreme curvature of the path make it a poor solution formotion control applications.

[0019] Moreover, accurate space-filling is often neither attainable nordesirable in motion planning. What is needed are trajectories that passwithin a specified distance of any point of the region of interest atany given time.

[0020]FIG. 1B—Boustrophedon Scanning Path

[0021] One widely used scanning strategy is referred to as theboustrophedon path or “way of the ox”. An example of this approach ispresented in FIG. 1B. The chief advantages of the boustrophedon path arethe simplicity of the definition and the possibility to fill gaps infurther loops. However, there are significant drawbacks to this scheme.In particular, the scanning cannot be done continuously because ofcurvature problems. For example, at each end of a long scan line, two 90degree turns must be made. Such abrupt changes in motion are problematicfor most motion control systems. Theoretically, the average arrival timeis much worse than the strategies of the present invention, describedbelow. The drawbacks of the boustrophedon approach are even moredramatic when searching for small objects of unknown size.

[0022]FIG. 1C—Archimedes Spiral Scanning Path

[0023] The other widely used scanning strategy in practical applicationsis based on an Archimedes spiral. FIG. 1C illustrates one example of anArchimedes Spiral-based scanning path. As FIG. 1C demonstrates, thecurvature is unbalanced, with high curvature near the center of thespiral, and low curvature near the outer edges. Additionally, thisapproach clearly lends itself to scanning circular regions, and istherefore unsuitable for non-circular scan regions. Other drawbackssimilar to those of boustrophedon paths also apply to the ArchimedesSpiral scheme, such as fixed scanning resolution or path width, whichmay not be suitable when scanning for small objects. Moreover, as may beseen in FIG. 1C, much time is spent scanning regions away from thecenter of the region.

[0024]FIG. 1D—Rectangular Spiral Scanning Path

[0025]FIG. 1D illustrates a scanning scheme which utilizes features ofboth the boustrophedon and the spiral path approach. As FIG. 1D shows,the path comprises concentric straight-line path segments which spiraloutward from the center of the region. This scheme, however, suffersfrom some of the shortcomings of its predecessors. For example, similarto the boustrophedon approach, there are discontinuities in the path ateach corner, leading to sudden accelerations of the scanning apparatus.Furthermore, the path resolution is fixed, as with the ArchimedesSpiral, and may therefore be unsuitable for objects of various orunknown sizes.

[0026] In almost all practically relevant cases, scanning schemes formore complicated geometries are based on boustrophedon paths or on othercombinations of lines. This is particularly true when obstacles are partof the environment. Such obstacles may be described as holes in givenregions. Topologically complex geometries, such as open sets (as opposedto simply connected objects) may also be scanned. The standard procedureis to go back-and-forth between boundaries of the resulting regions. AsFIG. 1B demonstrates, such an approach is neither optimal nor acceptablefrom a motion control standpoint, for at least the reasons given above.

[0027] Therefore, improved systems and methods for scanning a region aredesired. More specifically, scanning methods are needed whichefficiently and thoroughly scan a region, possibly subject to specificcurvature constraints.

SUMMARY OF THE INVENTION

[0028] The present invention comprises various embodiments of a system,method, and memory medium for scanning for an object within a region, orfor locating a point within a region. Embodiments of the inventioninclude a method for scanning for an object within a region using aconformal scanning scheme, a method for scanning for an object within aregion using a Low Discrepancy Sequence scanning scheme, a method forscanning for an object within a region using a Low Discrepancy Curvescanning scheme, and a method for locating a point of interest in aregion.

[0029] Conformal Mapping Embodiment

[0030] One embodiment of the invention comprises a method for scanningfor an object within a region using a conformal scanning scheme. Theregion may have a dimensionality of one of one, two, or three, or mayhave a dimensionality greater than three. This method may first involvedetermining the characteristic geometry of the region. The method maythen generate a conformal scanning curve based on the characteristicgeometry of the region. Generation of the conformal scanning curve maycomprise performing a conformal mapping between the characteristicgeometry and a first scanning curve to generate the conformal scanningcurve. The first scanning curve may be designed to minimize angledeviations and/or may be an optimum scanning curve for a first geometry,e.g., different from the characteristic geometry. The resultingconformal scanning curve may have a maximum curvature below a specifiedcurvature value.

[0031] The method may then scan the region using a conformal scanningscheme based on the conformal scanning curve, i.e., may measure theregion at a plurality of points along the conformal scanning curve.These measurements of the region produce data indicative of one or morecharacteristics of the object. The method may then examine the resultingdata generated from the scan to determine one or more characteristics ofthe object and generate output indicating the one or morecharacteristics of the object.

[0032] Low Discrepancy Sequence Scanning

[0033] One embodiment of the invention comprises a method for scanningfor an object within a region using a Low Discrepancy Sequence scanningscheme.

[0034] The method may first involve calculating a Low DiscrepancySequence of points in the region. The region may have a dimensionalityof one or two, or the region may have a dimensionality greater than two.Calculation of the Low Discrepancy Sequence of points in the region maycomprise determining a plurality of points, wherein any location in theregion is within a specified distance of at least one of the LowDiscrepancy Sequence of points. The method may then generate a motioncontrol trajectory from the Low Discrepancy Sequence of points.

[0035] Generation of the motion control trajectory may comprisegenerating a Traveling Salesman Path (TSP) from the Low DiscrepancySequence of points, wherein the TSP includes each point of the LowDiscrepancy Sequence of points, and then re-sampling the TSP to producea sequence of motion control points comprising the motion controltrajectory. Generation of the Traveling Salesman Path may compriseapplying Lin's Nearest Neighbor algorithm to the Low DiscrepancySequence of points to generate the Traveling Salesman Path. The TSP maycomprise a first sequence of points, wherein the first sequence ofpoints defines a first trajectory having a first maximum curvature.Re-sampling the TSP may comprise manipulating the first sequence ofpoints, which may involve adjusting point locations, discarding points,and/or generating new points, to produce the sequence of motion controlpoints. The sequence of motion control points may define a secondtrajectory having a second maximum curvature which is less than thefirst maximum curvature. In one embodiment, the sequence of motioncontrol points is a superset of the first sequence of points.Alternatively, the sequence of motion control points may comprise asubset of the first sequence of points and one or more additionalpoints.

[0036] The method may then scan the region along the motion controltrajectory, e.g., may measure the region at a plurality of points alongthe motion control trajectory. The method may then determine one or morecharacteristics of the object in response to the scan, and the methodmay generate output indicating the one or more characteristics of theobject.

[0037] Low Discrepancy Curve Scanning

[0038] One embodiment of the invention comprises a method for scanningfor an object within a region using a Low Discrepancy Curve scanningscheme.

[0039] The method may first involve generating a Low DiscrepancySequence of points in the region. Generation of the Low DiscrepancySequence of points in the region may comprise generating a plurality ofpoints, wherein any location in the region is within a specifieddistance of at least one of the Low Discrepancy Sequence of points. Themethod may then involve calculating a Low Discrepancy Curve in theregion based on the Low Discrepancy Sequence of points. In oneembodiment, generation of the Low Discrepancy Sequence of points in theregion and calculation of the Low Discrepancy Curve in the region basedon the Low Discrepancy Sequence of points are performed offline in apreprocessing phase of the method.

[0040] After the Low Discrepancy Curve has been generated, the methodmay scan the region using the Low Discrepancy Curve. The scanning may beperformed after the object is present in or enters the region. Thescanning may comprise measuring the region at a plurality of pointsalong the Low Discrepancy Curve. The scanning may be performed todetermine one or more characteristics of the object. The method may thengenerate output indicating the one or more characteristics of the objectresulting from the scan. Scanning or measuring the region along the LowDiscrepancy Curve, as well as determining one or more characteristics ofthe object and generating output, may be performed in a real time phaseof the method.

[0041] Generation of the Low Discrepancy Curve may be performed invarious ways. In one embodiment, for each successive pair of the LowDiscrepancy Sequence of points, the method may: 1) determine one or moreorthogonal line segments which connect the pair of points; and 2)re-sample the one or more orthogonal line segments to generate a LowDiscrepancy Curve segment. The Low Discrepancy Curve may comprise acontiguous sequence of the Low Discrepancy Curve segments from thesuccessive pairs of the Low Discrepancy Sequence of points. In otherwords, the Low Discrepancy Curve segments corresponding to thesuccessive pairs of the Low Discrepancy Sequence of points may besequentially connected to form the Low Discrepancy Curve.

[0042] In one embodiment, the one or more orthogonal line segments maycomprise a first sequence of points, wherein the first sequence ofpoints defines a first trajectory having a first maximum curvature. Inthis embodiment, re-sampling the one or more orthogonal line segmentsmay comprise manipulating the first sequence of points, which mayinvolve adjusting point locations, discarding points, and/or generatingnew points, to generate the Low Discrepancy Curve segment. The resultingLow Discrepancy Curve segment may define a second trajectory having asecond maximum curvature which is less than the first maximum curvature.

[0043] The re-sampling performed on the one or more orthogonal linesegments may also comprise fitting a curve to a plurality of pointscomprised in the plurality of orthogonal line segments subject to one ormore constraints, and then generating a second plurality of points alongthe fit curve, wherein the second plurality of points define the LowDiscrepancy Curve segment.

[0044] Calculation of the Low Discrepancy Curve in the region may beperformed in various ways, depending on the dimensionality of theregion. In one embodiment, the region may be defined by a coordinatespace having a plurality of orthogonal axes, wherein each of theplurality of orthogonal axes corresponds respectively to a dimension ofthe region. Each of the pair of points may have a plurality ofcoordinates corresponding respectively to the plurality of orthogonalaxes. Also, each of the plurality of line segments may be parallel to arespective one of the orthogonal axes. Thus, each of the plurality ofline segments may have a first endpoint and a second endpoint, whereinthe first endpoint has a first plurality of coordinates, the secondendpoint has a second plurality of coordinates, and wherein said firstplurality of coordinates and said second plurality of coordinates differonly in value of a coordinate corresponding to a respective one of theplurality of orthogonal axes.

[0045] In one implementation, the one or more orthogonal line segmentswhich connect the pair of points may comprise a contiguous sequence ofthe line segments corresponding to a specified order of the plurality oforthogonal axes. In this implementation, re-sampling the one or moreorthogonal line segments to generate the Low Discrepancy Curve segmentmay comprise re-sampling the contiguous sequence of the line segments inthe specified order to generate the Low Discrepancy Curve segment.

[0046] In an embodiment wherein the region is a 2-dimensional space, theplurality of orthogonal axes comprises an x axis and a y axis, and theplurality of line segments may comprise two orthogonal line segments,e.g., a first line segment and a second line segment. For example, afirst of the pair of points may have two coordinates, (x0, y0),corresponding respectively to the x and y axes, and a second of the pairof points may have two coordinates, (x1, y1), corresponding respectivelyto the x and y axes. Each of the line segments may have a first endpointand a second endpoint, wherein the second endpoint of the first linesegment is equal to the first endpoint of the second line segment. Thetwo orthogonal line segments which connect the pair of points maycomprise a contiguous sequence of the line segments, preferably in thespecified order. Also, re-sampling the two orthogonal line segments togenerate a Low Discrepancy Curve segment may comprise re-sampling thecontiguous sequence of the line segments in the specified order togenerate the Low Discrepancy Curve segment. Where the specified order ofthe plurality of orthogonal axes is (x, y), the first endpoint of afirst of the two line segments has coordinates (x0, y0), the secondendpoint of the first of the two line segments has coordinates (x1, y0),the first endpoint of a second of the two line segments has coordinates(x1, y0), and the second endpoint of the second of the two line segmentshas coordinates (x1, y1). Where the specified order of the plurality oforthogonal axes is (y, x), the first endpoint of a first of the two linesegments has coordinates (x0, y0), the second endpoint of the first ofthe two line segments has coordinates (x0, y1), the first endpoint of asecond of the two line segments has coordinates (x0, y1), and the secondendpoint of the second of the two line segments has coordinates (x1,y1);

[0047] In an embodiment wherein the region is a 3-dimensional space, theplurality of orthogonal axes comprises an x axis, a y axis, and a zaxis, and the plurality of line segments may comprise three orthogonalline segments, e.g., a first line segment, a second line segment, and athird line segment. For example, a first of the pair of points may havethree coordinates, (x0, y0, z0), corresponding respectively to the x, y,and z axes, and a second of the pair of points may have threecoordinates, (x1, y1, z1), corresponding respectively to the x, y, and zaxes. Each of the line segments may have a first endpoint and a secondendpoint, wherein the second endpoint of the first line segment is equalto the first endpoint of the second line segment, and wherein the secondendpoint of the second line segment is equal to the first endpoint ofthe third line segment. The three orthogonal line segments which connectthe pair of points may comprise a contiguous sequence of the linesegments, preferably in the specified order. Also, re-sampling the threeorthogonal line segments to generate a Low Discrepancy Curve segment maycomprise re-sampling the contiguous sequence of said line segments inthe specified order to generate the Low Discrepancy Curve segment.

[0048] In one embodiment, the method is operable to dynamically generatenew Low Discrepancy Curve segments during the scan and scan the regionalong these new Low Discrepancy Curve segments until desiredcharacteristics of the object are identified. In this embodiment, themethod may be operable to first generate an initial Low DiscrepancySequence of points in the region and calculate an initial LowDiscrepancy Curve segment in the region based on the first LowDiscrepancy Sequence of points. These steps may be performed in apre-processing phase. The method may then scan a portion of the regionalong the first Low Discrepancy Curve segment to attempt to identify adesired characteristic of the object. If the characteristic of theobject is not identified, then the method may dynamically generate andadd one or more new Low Discrepancy Sequence points in the region basedon previous Low Discrepancy Sequence points, calculate one or more newLow Discrepancy Curve segments in the region based on the one or morenew Low Discrepancy Sequence of points, and scan a portion of the regionalong the one or more new Low Discrepancy Curve segments to attempt toidentify the characteristic of the object. The method may be operable todynamically generate new Low Discrepancy Sequence points, calculate anew Low Discrepancy Curve segment, and scan the region along this newLow Discrepancy Curve segment one or more times in an iterative fashionuntil the desired characteristics in the region are identified, or untilan iteration threshold has been reached.

[0049] Generating a Low Discrepancy Curve

[0050] One embodiment of the invention comprises a method for generatinga Low Discrepancy Sequence curve in a region, such as a 2D rectangle, orthe unit square. The method may be performed by a computer comprising aCPU and a memory medium. The memory medium may be operable to store oneor more computer programs which are executable by the CPU to performvarious embodiments of the method.

[0051] In one embodiment, the method may include generating an unboundedLow Discrepancy Point. As used herein, the term unbounded LowDiscrepancy Point refers to a generated Low Discrepancy Point which mayor may not fall within the bounds of the region. One or more boundaryconditions may then be applied to the unbounded Low Discrepancy Point togenerate a bounded Low Discrepancy Point, wherein the bounded LowDiscrepancy Point is located within the region. In one embodiment, thegenerating an unbounded Low Discrepancy Point and the applying one ormore boundary conditions may be repeated one or more times to generate aLow Discrepancy Sequence in the region. The generated Low DiscrepancySequence may then be stored, and output generated, comprising the LowDiscrepancy Sequence, wherein the Low Discrepancy Sequence defines thecurve in the region. In one embodiment, each bounded Low DiscrepancyPoint of the Low Discrepancy Sequence may be store as it is generated.In one embodiment, the curve is a Low Discrepancy Curve. The method mayfurther include scanning the region according to the curve defined bythe Low Discrepancy Sequence.

[0052] In one embodiment, generating the unbounded Low Discrepancy Pointmay include selecting two or more irrational numbers, a step sizeepsilon (ε), and a starting position, initializing a current position tothe starting position, and incrementing one or more terms of the currentposition based on a factor of ε and one of the irrational numbers, wherethe incremented position is the unbounded Low Discrepancy Point. In oneembodiment, the starting position may be a randomly selected point inthe region. As described above, because the unbounded Low DiscrepancyPoint may fall outside the region, the one or more boundary conditionsmay be applied, generating the bounded Low Discrepancy Point, and thecurrent position may be set to the bounded Low Discrepancy Point. Itshould be noted that in the iteration described above, theinitializations or selections are only performed once at the beginning,i.e., the repeating said generating and said applying boundaryconditions one or more times preferably comprises repeating saidincrementing, said applying one or more boundary conditions, and saidsetting the current position, one or more times.

[0053] In one embodiment, the method may also include selecting amaximum length L of the curve in the region and initializing a currentlength to zero prior to said repeating. At each iteration, the currentlength may be updated to include a distance from the current position tothe generated bounded Low Discrepancy Point. In the preferredembodiment, said repeating one or more times comprises repeating untilthe current length meets or exceeds the maximum length L.

[0054] In one embodiment, applying one or more boundary conditions maycomprise checking if the unbounded Low Discrepancy Point is outside ofthe region, and if so, applying one of a reflecting boundary conditionor a toroidal boundary condition at each border of the region.

[0055] Note that although the above describes an embodiment wherein theregion comprises a 2-dimensional rectangular region, the two or moreirrational numbers comprise two irrational numbers, and the curve in theregion comprises one or more line segments, it is also contemplated thatmore complex curves may be generated, and that higher dimensionalregions, such as unit hyper-cubes, may be used by various embodiments ofthe method.

[0056] Generating a Curve on a Surface

[0057] One embodiment of the comprises a method for generating a curve,such as a Low Discrepancy Curve, on a surface. In the preferredembodiment, the surface may be an abstract surface with a Riemannianmetric. In one embodiment, the curve may be generated in a simple space,then mapped to the surface.

[0058] A parameterization of the surface may be selected. In thepreferred embodiment, the parameter space for the parameterization isthe unit square or a rectangle. Other suitable geometries for theparameter space are also contemplated, including higher dimensional unitcubes and rectangles, among others. A first curve in the parameter spacemay be selected, e.g., a Low Discrepancy Curve. Then, are-parameterization of the surface may be determined or generated. Forexample in a preferred embodiment, a re-parameterization of the surfacemay be determined such that a ratio of line and area elements of thesurface based on a Riemannian metric is constant.

[0059] The generated curve may be mapped onto the surface using there-parameterization. For example, in one embodiment, the generated LowDiscrepancy Curve in the unit square may be mapped onto the surface.

[0060] Finally, output may be generated comprising the mapped curve,e.g., the mapped Low Discrepancy Curve. In one embodiment, generatingthe output may comprise storing the curve for later use. In anotherembodiment, generating the output may comprise displaying the curve on adisplay device.

[0061] Thus, by using the above-described method, a curve, such as a LowDiscrepancy Curve, generated on a unit square (or other suitablegeometry) may be mapped to an abstract surface. It should be noted thatany sequence, e.g., LDS, or curve, e.g., LDC generated on the unitsquare (or other suitable geometry) may be mapped in this way. In otherwords, it is not required that the sequence or curve be generated in anyparticular manner.

[0062] Precise Location of a Point of Interest

[0063] One embodiment of the invention comprises a method fordetermining a precise location of a point of interest in a region. Inone embodiment, an approximate model of the region is known, and themethod utilizes knowledge of this model. The region of interest maycomprise a data distribution, and the point of interest may comprise anextremum of the data distribution. For example, the data distributionmay comprise a Gaussian distribution, and the point of interest maycomprise a Gaussian peak of the Gaussian distribution.

[0064] In one embodiment, the method may first determine or locate aregion of interest in the region. Location of the region of interest maybe performed in various ways, and one method is described below. Themethod may then determine one or more characteristics of the region ofinterest within the region, wherein the region of interest includes thepoint of interest. The one or more characteristics of the region ofinterest may comprise a radius of the region of interest. The one ormore characteristics of the region of interest may also or insteadcomprise an approximate location of the point of interest, e.g., acenter of the region of interest.

[0065] The method may then determine a continuous trajectory based onthe one or more characteristics of the region of interest, wherein thecontinuous trajectory allows measurement of the region of interest. Themethod may then measure the region of interest at a plurality of pointsalong the continuous trajectory to generate a sample data set. Themethod may then perform a surface fit of the sample data set using theapproximate model to generate a parameterized surface. The method maythen calculate a location of the point of interest based on theparameterized surface. The method may optionally measure the region ofinterest at the point of interest to confirm correctness of thecalculated location. Finally, the method may generate output indicatingthe calculated location of the point of interest, or the calculatedlocation of the point of interest may be provided to another program foruse.

[0066] Locating the region of interest in the region may be performed invarious ways. For example, the method may scan the region to locate twoor more points of the region of interest, wherein each of the two ormore points has associated measured data. The method may then determinea local point of interest in the region of interest proximate to the twoor more points of the region of interest.

[0067] The two or more points of the region of interest may comprise anentry point and an exit point of the region of interest. In this case,the method may scan along a first scan line between the entry point andthe exit point to determine the local point of interest, calculate asecond scan line, wherein the second scan line passes through the localpoint of interest and is orthogonal to the first scan line, and measurethe region along the second scan line to generate second scan lineassociated measured data. The method may then determine a second localpoint of interest along the second scan line based upon the second scanline associated measured data, determine a center of the region ofinterest based upon one or more of the second local point of interestand the first local point of interest, and provide a radius, wherein theregion of interest comprises an area of the region within the radius ofthe determined center.

[0068] A system may implement any of the above methods for scanning foran object within a region, for locating a point of interest in a region,or for generating curves in a region. The system may comprise a computersystem coupled to a sensor. The computer system may comprise a CPU and amemory medium, or programmable logic, which is operable to store ascanning program that implements one of the above methods. An embodimentof each of the above invention(s) may also be a software program orprograms stored on a memory medium.

BRIEF DESCRIPTION OF THE DRAWINGS

[0069] A better understanding of the present invention can be obtainedwhen the following detailed description of the preferred embodiment isconsidered in conjunction with the following drawings, in which:

[0070] FIGS. 1A-D illustrate various scanning paths, according to theprior art;

[0071]FIG. 2A illustrates a scanning system, according to one embodimentof the present invention;

[0072]FIG. 2B is a block diagram of the computer system of FIG. 2A,suitable for implementing various embodiments of the invention.

[0073]FIG. 3A illustrates a machine vision application of a scanningsystem, according to one embodiment of the present invention;

[0074]FIG. 3B illustrates a robotics application of a scanning system,according to one embodiment of the present invention;

[0075]FIG. 3C illustrates a phased array control application of ascanning system, according to one embodiment of the present invention;

[0076]FIG. 3D illustrates an optical fiber alignment system, accordingto one embodiment of the present invention;

[0077] FIGS. 4A-C illustrate limitations of scanning schemes withcurvature constraints;

[0078]FIG. 4D illustrates a smooth transition between two circular scancurves of differing radii, according to one embodiment;

[0079]FIG. 4E illustrates a smooth transition between two semi-circularscan curves of equal radius, according to one embodiment;

[0080]FIG. 4F illustrates a smooth transition between two unit circles,according to one embodiment;

[0081]FIG. 5A illustrates a continuous scan curve with boundedcurvature, according to one embodiment;

[0082]FIG. 5B illustrates a limitation of a scan curve with boundedcurvature, according to one embodiment;

[0083]FIG. 6 illustrates a conformal spiral scan curve, according to oneembodiment;

[0084]FIG. 7 is a flowchart of a conformal scanning process, accordingto one embodiment;

[0085]FIG. 8A illustrates a Halton Sequence and a random distribution ofpoints;

[0086]FIG. 8B illustrates a Low Discrepancy Sequence Path and a SplinedLow Discrepancy Sequence Path, according to one embodiment;

[0087]FIG. 9 is a flowchart of a Low Discrepancy Sequence scanningprocess, according to one embodiment;

[0088]FIG. 10 illustrates component regions used to define a LowDiscrepancy Sequence, according to one embodiment;

[0089] FIGS. 11A-11C illustrate various straight line paths forming anLDC on a unit square;

[0090]FIGS. 12A and 12B flowchart embodiments of a process forgenerating a Low Discrepancy Curve scan path in a region, according toone embodiment;

[0091]FIG. 13A illustrates a Low Discrepancy Curve, according to oneembodiment;

[0092]FIG. 13B illustrates a Splined Low Discrepancy Curve, according toone embodiment;

[0093]FIG. 13C illustrates a comparison of travel distances for aConformal Spiral path and a Low Discrepancy Curve path, according to oneembodiment;

[0094]FIG. 13D illustrates a comparison of travel distance bias for aConformal Spiral path and a Low Discrepancy Curve path, according to oneembodiment;

[0095]FIG. 14 is a flowchart of a Low Discrepancy Curve scanningprocess, according to one embodiment;

[0096] FIGS. 15A-15D illustrate scan paths for various surfaces;

[0097]FIG. 16 illustrates a spherical spatial scan path, according toone embodiment;

[0098]FIG. 17 is a flowchart of a method for mapping a Low DiscrepancyCurve to an abstract surface, according to one embodiment;

[0099]FIG. 18 illustrates a 3d surface to be scanned, according to oneembodiment;

[0100]FIG. 19 illustrates an initial Splined Low Discrepancy Curve basedcoarse search followed by a refined final approach, according to oneembodiment;

[0101]FIG. 20 illustrates a Gaussian-like intensity field distribution,according to one embodiment;

[0102]FIGS. 21A and 21B illustrate a final approach strategy, accordingto one embodiment;

[0103]FIG. 21C illustrates error distributions of the estimated peak Xand Y coordinate errors, according to one embodiment;

[0104]FIG. 22 is a flowchart of a final approach of a scanning process,according to one embodiment; and

[0105]FIG. 23 is a flowchart process for locating a region of interestin a scan region, according to one embodiment.

[0106] While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and are herein described in detail. It should beunderstood, however, that the drawings and detailed description theretoare not intended to limit the invention to the particular formdisclosed, but on the contrary, the intention is to cover allmodifications, equivalents and alternatives falling within the spiritand scope of the present invention as defined by the appended claims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0107]FIG. 2A—A Scanning System

[0108]FIG. 2A illustrates a scanning system according to one embodimentof the present invention. As FIG. 2A shows, a computer system 102 may becoupled to a camera or sensor 110 which is operable to acquire opticalor image information from an object 120. In one embodiment, the scanningsystem may also include a motion control component 125, such as a motioncontrol stage, which may be operable couple to the computer system 102.The motion control state may receive control signals from the computersystem 102 and may move the object 120 with respect to the camera/sensor110 for scanning purposes. In another embodiment, the motion controlcomponent may move the camera/sensor 110 instead of the object 120 toscan the object 120.

[0109] The sensor 110 may also be referred to as a remote scanningdevice. The sensor 110 may be a camera or image acquisition device,which may be adapted to receive one or more portions of theelectromagnetic (EM) spectrum, e.g., visible light, infrared, orultraviolet (UV) light. The sensor 110 may also be an ultrasonic devicefor detecting sound waves. Thus the sensor 100 may use any of varioustechniques to scan and produce the image data, including visible light,infrared, ultrasonic, light interferometer, and other non-contact andcontact methods.

[0110] In one embodiment, the computer system 102 may operate to “scan”previously acquired data, e.g., scan stored data in a data miningapplication. In this instance, the system may not require sensor 110 forscanning a physical object or region, as the data being analyzed hasbeen previously acquired and stored.

[0111] The computer system 102 may include a display device, such as amonitor, as well as a chassis and one or more I/O devices, such as akeyboard and/or mouse. However, the computer system 102 may take any ofvarious forms, such as a personal computer, or any type of device whichincludes a processor that executes instructions from a memory medium, orwhich includes programmable logic that has been configured to performthe methods of the present invention. Exemplary computer systems includea personal computer, mainframe computer, a personal computing device(PDA), television, embedded device, and other systems. Thus, as usedherein, the term computer system is intended to encompass any of variousdevices which include a processor that can execute instructions from amemory medium and/or may include a programmable logic device that can beconfigured to execute a scanning method or algorithm.

[0112] Thus, the method of the present invention may be implemented inany of various types of devices and any of various types ofapplications. Example applications where the method described herein maybe used include phased array systems, industrial automation ormanufacturing, robotics, machine vision systems, and any otherapplication where it is desirable scan an object to acquire datacharacterizing the object.

[0113] In this example, one sensor 110 (a camera in this example)scanning one object 120 is shown, but in other embodiments any number ofsensors 110 or cameras may be used to scan any number of objects 120.The camera 110 may comprise any type of camera or device operable toacquire images of the object 120. The objects 120 may be stationary, ormay be conveyed into and out of the field of view of the one or morecameras 110.

[0114] The camera 110 may be operable to output a video stream tocomputer system 102, e.g., to an image acquisition (IMAQ) devicecomprised in the computer system 102. The computer system 102 may thenanalyze the images captured by the image acquisition board.Alternatively, the image acquisition board may include an on-boardprocessor and memory for performing a portion or all of the imageanalysis.

[0115] The computer system 102 may use or store scanning software whichanalyzes the image data received from the camera 110, and controls thescanning operation, i.e., determines the scan path over the object. Thesystem may also comprise one or more motion systems for moving thecamera, the object, or both, in order to implement the scanning scheme.The motion system may be a motion control system, such as is availablefrom National Instruments Corporation.

[0116]FIG. 2B—Computer System Block Diagram

[0117]FIG. 2B is a block diagram of a computer system 102 which may beused to implement various embodiments of the scanning schemes describedabove. As FIG. 2B shows, the computer system 102 may include a memory204 which is operable to store one or more software programs forimplementing the scanning methodologies described above. The computersystem 102 may also include an Input/Output (I/O) interface 206 forcommunication with external systems, and a CPU 208 which may be operableto execute the one or more software programs for implementing thescanning methodologies. Thus, the computer system 102 may store and/orexecute one or more software programs which perform the methodsdescribed above with reference to FIGS. 4-23.

[0118] In one embodiment, the computer system 102 may include a displaydevice, such as a monitor, as well as a chassis and one or more I/Odevices, such as a keyboard and/or mouse. However, the computer systemmay take any of various forms, such as a personal computer, or any typeof device which includes a processor that executes instructions from amemory medium, or which includes programmable logic that has beenconfigured to perform the methods described in FIGS. 4-23. Exemplarycomputer systems include a personal computer, mainframe computer, apersonal computing device (PDA), television, embedded device, and othersystems. Thus, as used herein, the term computer system is intended toencompass any of various devices which include a processor that canexecute instructions from a memory medium and/or may include aprogrammable logic device that can be configured to execute a method oralgorithm, such as that described in FIGS. 4-23.

[0119] Thus, the method of the present invention may be implemented inany of various types of devices and any of various types ofapplications. Example applications where the method described herein maybe used include inspection systems, industrial automation or motioncontrol systems, measurement systems, machine vision systems and anyother application where it is desirable to scan an object or space. Morespecific applications wherein the method of the present invention may beused include robotics, and phased array control systems, as well asscanning related to image data, measurement data, acoustic data, seismicdata, financial data, stock data, futures data, business data,scientific data, medical data, and biometric data, among others.

[0120] FIGS. 3A-D—Example Applications of the Scanning System

[0121] FIGS. 3A-D illustrate various exemplary applications wherevarious embodiments of the present invention may be used. However, it isnoted that the invention is not limited to these applications, butrather may be used in any of various applications.

[0122]FIG. 3A—Machine Vision Application of the Present Invention

[0123] In a machine vision or automated inspection application of thepresent invention, shown in FIG. 3A, a system similar to that shown inFIG. 2 may implement the present scanning methodology in software and/orhardware for quality control in a manufacturing process. As FIG. 3Ashows, one or more cameras 110A-D may be coupled to computer system 102Afor scanning objects from several points of view. In this example,objects 120 are carried past the one or more cameras 110 bymanufacturing apparatus 106. The system may operate to scan each object120, and the images received from each camera 110 may be analyzed usingimage processing software executing on the computer system 102A. Theanalyses of the images may be used to detect defects or othercharacteristics of the object 120. For example, in various applicationsthe analyses may be designed to detect one or more of: physical surfacedefects (scratches, etc.); one or more components located correctly onthe object; a correct label on the object; a correct marking on theobject; correct color information on the object, etc. Thus, in a machinevision manufacturing application, the results of the image analyses maybe used to determine whether an object meets desired productionstandards. This determination may be performed in any of various ways,as desired for a particular application. If the object does not meet thedesired production standards, the object may be rejected, as shown inthe case of object 122.

[0124]FIG. 3B—Robotics Application of the Present Invention

[0125]FIG. 3B illustrates an example application of the presentinvention in the field of robotics. As FIG. 3B shows, a computer system102B may be operable to control one or more robotic arms 114, eachcomprising a camera 110, to scan an object 120. The computer system 102Bmay be operable to store and execute software implementing a scanningscheme according to the present invention. More specifically, thecomputer system 102B may be operable to store and execute one or moresoftware programs to calculate one or more scanning paths based uponuser input and/or sensor data. In one embodiment, the path calculationsmay be performed offline in a preprocessing phase. In anotherembodiment, part or all of the path calculations may be performed inreal time. The computer system 102B may be further operable to store andexecute software programs to maneuver the one or more robotic arms 114and respective cameras 110 to implement the calculated scanning scheme.

[0126] In one embodiment of the system shown in FIG. 3B, multiplerobotic arms may be used in tandem. In this case, a cooperative scanningstrategy may be required which coordinates the movement of each arm 114to collectively scan the object 120.

[0127]FIG. 3C—Phased Array Application of the Present Invention

[0128]FIG. 3C illustrates an example application of the presentinvention in the area of phased array control. A phased array typicallyrefers to a group of antennas in which the relative phases of therespective signals feeding the antennas are varied in such a way thatthe effective radiation pattern of the array is reinforced in a desireddirection and suppressed in undesired directions. As FIG. 3C shows,computer system 102C may couple to a phased array 302. The phased array302 may comprise a plurality of array elements 304 which may each becontrolled independently or in concert with the other array elements304. The computer system 102C may store and execute software which isoperable to control the phased array elements to accomplish a specifictask. Other examples of controlled phased arrays include telescope farmsand micro-mirror assemblies on fiber optic transfer chips.

[0129]FIG. 3D—Optical Fiber Alignment Application of the PresentInvention

[0130]FIG. 3D illustrates an example machine motion application wherethe goal is a fast procedure for precise alignment of two opticalfibers. In this example application, a laser source 310 generates a beam312 which is routed into a first fiber 320A and checked or measuredthrough a second fiber 320B, where the intensity of the laser beam 312is constantly measured and used to align the two fibers 320. Furtherdetails of this procedure are presented below with reference to FIG. 14,as well as under the section titled “Applications and Test Results”.

[0131] Theory

[0132] FIGS. 4A-C—Scanning Under Curvature Constraints

[0133]FIGS. 4A, 4B, and 4C illustrate various scanning schemes withcurvature constraints, as presented in the following Theorems andLemmas.

[0134] Motion control stages act in real environments and the laws ofphysics forbid many theoretically interesting trajectories. For example,if the movement apparatus is very massive, the inertia of the system mayrestrict allowable acceleration values. In other words, parametersspecifying start/stop events and path curvature may be constrained toparticular ranges for a given system. Such constraints may enable stagesto realize smooth movement where the travel velocity is constant oralmost constant. This property is desirable in that it allows aconnected data acquisition unit to take measurements on the fly, therebyreducing the complexity of the overall scanning procedure. If thetrajectory is allowed to leave the region of interest, possiblydifferent (and better) curves may be considered in a search for anoptimal curvature constrained solution, resulting in a new class ofscanning strategies.

[0135] In many motion planning routines smooth trajectories are anecessity. If an object must travel with constant or almost constantvelocity the laws of physics require continuous curvatures. The questionarises whether or not a specific geometry can be scanned under thecondition that the absolute value of the curvature of the trajectory iscontinuous and always in a given interval [l,u] where the upper value uis finite. Before dealing with this problem in detail the concept ofscanning a geometric object must be defined more accurately. Definition:

[0136] Let G be an open and connected region in R². A sufficientlysmooth curve C: R⁺−>G in natural parameterization scans G completely iffor all points g of G and for all ε>0 there is a point g(ε) on C thatthe distance between g and g(ε) is less than ε. In other words, C is analmost G-filling curve. Classical examples such as Peano or Hilbertcurves lack the smoothness condition.

[0137] This section deals with curves that scan certain open sets Gcompletely where the absolute value of the curvature of the underlyingsmooth curve is limited. Clearly, not all open regions G will allow sucha smooth scanning scheme, e.g. a square can not be scanned completelywithout violating the curvature constraint, or leaving the boundaries ofthe space. A proof of this limitation follows:

[0138] Lemma 1:

[0139] Let C: [0,S]−>R² be a smooth unit speed curve (x(s), y(s)) in R²starting at (0,0) where the normalized tangent at this point is (0,1).The curvature k(s) of C satisfies the inequality

−1≦k(s)≦1 for all s.

[0140] Let so be a first positive value with y(s₀)=1. Then the curveC/_([0,s0]) and the two circles C₁ and C₂ (see FIG. 4A) have no point incommon.

[0141] Let s₁ be the first positive value with cos(τ(s))=0. Then|x(s₁)|≧1/k*.

[0142] Proof:

[0143] Using the celebrated Fundamental Theorem of Plane curves:x(s) = ∫₀^(s)s  sin (τ(s)) + x₀  and  y(s) = ∫₀^(s)s  cos (τ(s)) + y₀where τ(s) = ∫₀^(s)sk(s) + ϑ₀  

[0144] The three constants x₀, y₀, τ₀ are equal to zero because of thegiven constraints. The function y(s) is monotonic in [0, s₀]. Thisfollows directly from the fact that −s≦τ(s)≦s and cos(τ(s))≧cos(s)≧0implies y(s)≧sin(s), in particular s₀≦{fraction (π/2)}. Furthermore,|x(s)|<1−cos(s) in [0, s₀]. It follows that for s in [0, s₀] the point(x(s), y(s)) is outside of C₁ and C₂. This proves the first part ofLemma 1.

[0145] Given the first positive s₁ with cos(τ(s₁))=0, it follows that|τ(s₁)|={fraction (π/2)} and $\begin{matrix}{{x( s_{1} )} = \quad {{\int_{0}}^{s_{1}}{{s}\quad {\sin ( {\tau (s)} )}}}} \\{= \quad {{\int_{0}}^{\tau {(s_{1})}}{{u}\quad \frac{\sin (u)}{k(s)}}}} \\{= \quad {{\int_{0}}^{\pi/2}{{u}\quad \frac{\sin (u)}{k(s)}}}}\end{matrix}$

[0146] thus

^(x)(s ₁)<1/k*.

[0147] q.e.d.

[0148] Theorem 1:

[0149] A square can not be scanned by a smooth curve C: R⁺−>R²completely under a given curvature constraint |k(s)|≦k* for allnonnegative s.

[0150] Proof:

[0151] Let ε be a sufficiently small positive number and S_(ε) a cornerof size ε×ε of the given square where the smooth unit speed curve Cneither starts nor ends. Such a corner does exist. C scans the squarecompletely. So there is a sub-curve C_(ε) of C that enters and leavesS_(ε) (see FIG. 4B). If (x(s), y(s)) for s in [a,b] describes thissub-curve then two cases must be distinguished.

[0152] (A) At least one function x(s) or y(s) has a local extremum in[a, b].

[0153] (B) Both functions x(s) and y(s) are monotonic in [a, b].

[0154] In case (A) assume there is a local minimum of y(s) in [a, b] inthe lower left corner of the given square. According to FIG. 4B and toLemma 1 (where curvature of 1 is replaced by k* appropriately) the curveC must reach at least a y-value of ε−k*. This means C leaves the givensquare, which is a contradiction of the assumption.

[0155] In case (B) assume the situation depicted in FIG. 4C. Then one ofthe angles cc or β is at least {fraction (π/4)}. Choosing the smallerangle in conjunction with Lemma 1 guarantees that the curve C reaches anx-position less than ε−k*/sqrt(2) and leaves the square whichcontradicts the assumption.

[0156] q.e.d.

[0157] On the other hand, many other relevant regions can be scannedcompletely under the curvature constraint. The key to many of thesescanning strategies is to build curves based on smooth transitionsbetween circles. FIGS. 4D and 4E demonstrate these transitions for twotypical cases.

[0158] Such smooth transitions from a curve A to a curve B can beproduced by the following generic structure. Let A and B be described by

A: (x ₁(t), y ₁(t)) for 0≦t≦1 and

B: (x ₂(t), y ₂(t)) for 0≦t≦1, respectively.

[0159] The transition function is f(t)=−6t ⁵+15t ⁴−10t ³+1 for 0≦t≦1.f(t) can be extended by f(t)=1 for t<0 and f(t)=0 for t>1. The extensionis twice continuously differentiable. The curve f(t)A+(1−f(t))B realizesthe smooth transitions as shown in FIGS. 4D and 4E, described below.

[0160]FIG. 4D—Smooth Transition Between Circles of Different Radii

[0161]FIG. 4D illustrates a smooth transition between two circular scancurves of different radii. As FIG. 4D shows, a first circle of radius0.5 is located interior to a second circle of radius 1. A smoothtransition curve intersects each of the two circles tangentially, suchthat a scan path may include both circles (via the transition curve)without discontinuities.

[0162] As also shown in FIG. 4D, the curvature of the transition curveis bounded between 0.7 and 2.0, and has no discontinuities. It should benoted that the particular location and relative sizes of the circleshave been chosen for illustration purposes only, and that smoothtransitions in the manner described may be applied not only to othercircle pairs of varying radii and positions, but to pairs ofnon-circular curves, as well.

[0163]FIG. 4E—Smooth Transition Between Circles of Equal Radii

[0164]FIG. 4E illustrates a smooth transition between two circular scancurves of equal radii. As FIG. 4E shows, two semi-circular curves ofradius 1 intersect at an external tangent point. A smooth transitioncurve is shown which transitions from the left semi-circular curve tothe right semi-circular curve (or vice versa) at the tangent point, andwhich is also tangent to each of the semi-circular curves at the tangentpoint. In this example, the transition occurs at the transition curve'sinflection point, i.e., when the transition curve switches from convexto concave.

[0165] As also shown in FIG. 4E, the curvature of the transition curveis bounded between −1.3 and +1.3, and has no discontinuities. It shouldbe noted that the particular location and sizes of the semi-circularcurves have been chosen for illustration purposes only, and that smoothtransitions in the manner described may be applied not only to othersemi-circular curve pairs of varying radii and positions, but to pairsof non-circular curves, as well.

[0166] Theorem 2:

[0167] The whole plane can be scanned completely if the underlyingsmooth curve's maximum curvature is less than an arbitrarily chosen butfixed positive number.

[0168] Proof:

[0169] Assume a standard honeycomb tiling of the whole plane withdiameter 2. Then the set of all circles covering each hexagon containsall points of the plane. As FIG. 4F shows, a smooth transition betweentwo neighbors of this set of circles is possible where the curvature isalways less than a certain value (this value can be brought arbitrarilyclose to 1, but this fact is not essential here). A valid completescanning scheme visits any circle infinitely often and comes closer than½^(n) to any point of the given circle where the number n stands for then^(th) visit of this circle.

[0170] More interesting is the case of a circle of radius r, for whichthe following Theorem holds true.

[0171] Theorem 3:

[0172] A unit circle can be scanned completely with a smooth curve whosecurvature can be arbitrarily close to 2. There is no complete scanningscheme based on curvatures less than 2. This strategy is optimal in thesense that the curvature constraint is minimal (See FIG. 5, describedbelow).

[0173] Proof:

[0174] It may be proved first that the aforementioned scanning schemesdo exist. To this end it is shown that a smooth transition from theinner to the outer circle in FIG. 4D is possible where the curvature ofthe transition curve is always less than 2 (the curvature of the innercircle). It can also be shown that the length of the transition curvecan be made arbitrarily small without going beyond a curvature of 2.Based on this, one can concatenate two arbitrarily chosen inner circleswhere the curvature stays below 2. A valid scanning scheme chooses adense set of these inner circles and visits them one after another (SeeFIG. 5A below).

[0175] Now assume there is a scanning scheme of the unit circle wherethe curvature is always less than 2, say |k(s)|≦k*<2. C must comearbitrarily close to (0, 0) infinitely many times. Let ε be a smallnumber and (x_(ε), y_(ε)) a point of C that is in an ε-neighborhood of(0, 0). The curve C is oriented in such a way that the normalizedtangent of C in (x_(ε), y_(ε)) is (0,1). At least one of the branches ofC going through (x_(ε), y_(ε)) goes also through a local maximum ofy(s). Let s₂ be the nearest of these maximums (see FIG. 5B below).

[0176] According to the second part of Lemma 1 the absolute x-componentof this point is greater than 1/k*−ε. This point satisfies theassumptions of Lemma 1 and according to the first part of Lemma 1 thecurve C reaches an absolute x-component value beyond (1/k*−ε)+1/k*>1 forsufficiently small ε. This means C leaves the unit circle, whichcontradicts the assumption that C scans the unit circle completely.

[0177] q.e.d.

[0178] FIGS. 5A and B—Scanning With Minimal Curvature Constraints FIGS.5A and B illustrate a scanning scheme based on the above Theorem 3 andproof.

[0179]FIG. 5A illustrates a scanning scheme based on multiple circularscan path segments smoothly joined by transition curves. As FIG. 5Ashows, the maximum curvature is that of each circle, and so is dependentonly on the radius r of each circular scan path segment. Although thisexample shows the space (surface) covered by six circular scan pathsegments joined by six corresponding transition curves, the resolutionof the coverage may be increased by including more circular scan pathsegments, with corresponding transition curves. As FIG. 5A shows, thisapproach is particularly suitable for circular scan regions of radius 2r.

[0180] This scheme is utilized in one embodiment of the presentinvention as a final approach performed after an initial coarse search,described below with reference to FIG. 19.

[0181]FIG. 5B illustrates the construction of S2 and the subsequent partof the curve, as described above in the proof of Theorem 3.

[0182] Thus, it is possible to characterize 2d regions that can bescanned completely. Moreover, a sharp bound for the minimal absoluteupper curvature allowing such a scanning scheme can be calculated. It isfurthermore possible to show that numerous cross-free complete scanningschemes of given regions do exist but the upper absolute curvature valuealways tends to infinity. Further results deal with synchronous scanningschemes of regions where the different paths do not have any points incommon.

[0183] Scanning Without Curvature Constraints

[0184] The motivation for developing scanning schemes without curvatureconstraints is manifold. First of all, piecewise linear trajectories arevery common in motion control applications. The curvaturediscontinuities at the end points of the lines are often tolerable,especially for light payloads, e.g. in the form of astart-acceleration-constant velocity-deceleration-stop loop. Secondly,motion controllers are often capable of splining separate curvestogether. This may reduce curvature problems significantly with slightalterations of the original path shape.

[0185] Scanning without curvature constraints is less restrictive thanscanning with curvature constraints, and gives developers theopportunity to use numerous strategies. Described below are conformalmappings of given sampling strategies in specific geometries thatproduce new schemes in the image space. Scanning strategies based on thetheory of Low Discrepancy Sequences and the novel approach of LowDiscrepancy Curves are also described.

[0186]FIG. 6—Conformal Spirals

[0187] Grid-like scanning schemes are not ideal from a motion controlstandpoint. Vertices of the curves represent discontinuous firstderivatives where a smooth motion control based trajectory should haveat least two continuous derivatives. A grid-like scanning schemeautomatically means that a start-acceleration-constantspeed-deceleration-stop cycle must be repeated many times. An equallydistributed set of points lying on a curve is desirable. In this casethe travel time is shorter and the data acquisition task and the coupledanalysis routines are greatly simplified.

[0188] A compromise between the simplicity of a grid-like scanningscheme and the smoothness of the trajectory can be achieved when thetheory of conformal mappings is applied.

[0189] A mapping w=f(z) defined on a region D that is part of thecomplex plane is said to be angle preserving, or conformal, at z₀ if itpreserves angles between oriented curves in magnitude as well as inorientation. If f is a conformal mapping then orthogonal curves aremapped onto orthogonal curves. The following well known result shows inwhich regions a mapping defined by an analytic function is conformal.

[0190] Let f(z) be an analytic function in the domain D, and let z₀ be apoint in D. If f′(z)<>0, then f(z) is conformal at z₀.

[0191] According to the Riemann mapping theorem, there exists aconformal map from the unit disk to any simply connected planar region(except the whole plane). However, finding such a map for a specificregion is generally difficult. An important special case where a formulais known is when the target region is polygonal. In that case theSchwarz-Christoffel formula applies: $\begin{matrix}{{f(z)} = {{f(0)} + {c{\int_{0}^{z}{\prod\limits_{j = 1}^{n}\quad {( {\zeta - z_{j}} )^{a_{j} - 1}\quad {\zeta}}}}}}} & (1)\end{matrix}$

[0192] Here the polygon has n vertices, the interior angles at thevertices are πa₁, . . . , πa_(n) in counterclockwise order, and c is acomplex constant. The numbers z₁, . . . , z_(n) are the pre-images ofthe polygon's vertices, or pre-vertices, which lie in order on the unitcircle. Usually, these numbers must be computed separately.

[0193] Under some specific circumstances conformal mappings have asimple structure. The conformal transform mapping a circle onto a squarecan be described as a geometric morph of all points in the circle intocorresponding points in the square. More specifically, $\begin{matrix}{{{f(z)} = {c{\int_{0}^{z}{\frac{1}{\sqrt{\zeta^{4} + 1}}\quad {\zeta}}}}}{c = \frac{1}{\int_{0}^{1}{\frac{1}{\sqrt{s^{4} + 1}}\quad {s}}}}} & (2)\end{matrix}$

[0194] for all complex numbers z=a+jb with −1≦a, b≦1.

[0195] Given that an Archimedes spiral scans a circular region in anefficient manner, the same should be true for the image of this spiralwhen the conformal mapping (1) is applied. Conformal mappings preserveangles and curvature is defined as the change of angle along a givencurve.

[0196]FIG. 6 illustrates an Archimedes Spiral conformally mapped to asquare. As FIG. 6 demonstrates, the conformal Archimedes Spiral scans asquare more smoothly than the boustrophedon curve described above.

[0197]FIG. 7—Flowchart of Conformal Scanning Process

[0198]FIG. 7 is a flowchart of a conformal scanning process. An exampleof a conformal scan curve is described above with reference to FIG. 6.It should be noted that in some embodiments some of the steps presentedbelow may occur in a different order than shown, or may be omitted.

[0199] As FIG. 7 shows, in 702 the object to be scanned may be placed inthe scan region. In one embodiment, the object may appear in the scanregion automatically, such as in an inspection system where the objectis moved on a conveyor past the scanning apparatus. The object may bepaused in front of the scanner, or may simply be moved past slowlyenough that the scanning operation may be performed without stopping theobject's motion. In one embodiment, the presence of the object in thescan region may be undetermined, i.e., the region may be scanned todetermine whether the object is there.

[0200] In 704, a characteristic geometry of the scan region may bedetermined. For example, most scan regions tend to be basic geometricalshapes, such as circles, squares, rectangles, and so on.

[0201] In 706, a conformal scan curve may be generated based on thedetermined characteristic geometry. In one embodiment, the conformalscan curve may be generated by performing a conformal mapping betweenthe determined characteristic geometry and an existing scanning curve.In the preferred embodiment, the existing scanning curve is an efficientscanning curve which has a different characteristic geometry than thedetermined characteristic geometry of 704, i.e., the existing scanningcurve comprises a subset of points in a first geometry which isdifferent from the characteristic geometry of the scan region. In oneembodiment, the existing scanning curve may be an optimum scanning curvefor the first geometry. More specifically, in a preferred embodiment,the existing curve may be one which minimized angle deviations, whilecovering the first geometry efficiently. In other words, the curve maybe one which minimizes curvature, such as having a maximum curvaturebelow a specified curvature value.

[0202] In one embodiment, the conformal mapping between thecharacteristic geometry and the first scanning curve may be performedby 1) determining a mapping function which maps each point in the firstgeometry to a corresponding point in the determined characteristicgeometry; and 2) applying the mapping function to each point in thesubset of points in the first geometry to generate a correspondingsubset of points in the characteristic geometry, wherein the subset ofpoints in the characteristic geometry comprises the conformal scanningcurve.

[0203] For example, if the determined characteristic geometry of thescan region is a square, and the existing scanning curve is anArchimedes Spiral with a circular characteristic geometry, then aconformal mapping between the square and the existing curve generates asmooth ‘square’ spiral scan curve, as illustrated in FIG. 6. Of course,other characteristic geometries may produce different conformal curves.It should be noted that one of the primary features of conformalmappings is the preservation of angles, i.e., if a curve in a firstgeometry makes a forty degree angle with a feature, then the conformallymapped curve will make a forty degree angle with the conformally mappedfeature.

[0204] In 708, the region may be scanned using the generated conformalscan curve. In one embodiment, the scanning is performed by measuringthe region at a plurality of points along the conformal scan curve. Inone embodiment, the measurements may be made at equidistant points alongthe conformal scan curve.

[0205] In 710, one or more characteristics of the object may bedetermined in response to the scanning of 708. Examples of suchcharacteristics may include the object's precise location, shape,colors, identity, or any other detectable characteristic. In oneembodiment, measuring the region at the plurality of points along theconformal scanning curve produces data which may be examined or analyzedto determine the one or more characteristics of the object.

[0206] Finally, in 712, output indicating the one or morecharacteristics of the object may be generated. In one embodiment, theoutput may be displayed on a computer display. In another embodiment,the output may be transmitted to one or more internal or externalsystems. In another embodiment, the output may be stored in a computermemory for later analysis or use.

[0207] It should be noted that although the example given is for a twodimensional region and object, the method described above may be appliedto regions and objects of one, two, and three dimensions. In otherembodiments, the method may be applied to regions and objects ofdimensionality greater than three.

[0208]FIG. 8A—Low Discrepancy Sequences

[0209] Pseudo-random sequences have been used as a deterministicalternative to random sequences for use in Monte Carlo methods forsolving different problems. Recently, it was discovered that there is arelationship between Low Discrepancy Sets and the efficient evaluationof higher-dimensional integrals. Theory suggests that for midsizedimensional problems, algorithms based on Low Discrepancy Sets shouldoutperform all other existing methods by an order of magnitude in termsof the number of samples required to characterize the problem.

[0210] Given a function ƒ(x,) the problem of calculating the integral$\begin{matrix}{{I(f)} = {\int_{0}^{1}{{f(x)}\quad {x}}}} & (3)\end{matrix}$

[0211] in the most efficient manner is not a well posed problem. Anapproximate strategy may be based on the following procedure:

[0212] (A) Construct an infinite sequence {x₁, x₂, x₃, . . . , x_(,). .. } of real numbers in [0, 1] that does not depend on a specificfunction ƒ (nothing is known about ƒ in advance, except some generalsmoothness properties).

[0213] (B) During the nth step of the algorithm calculate ƒ(x_(n)) andthe approximation to the integral in (3) as:

I _(n)(f)=(f(x ₁)+ . . . +f(x _(n))/n  (4)

[0214] If a certain criterion is satisfied stop, else repeat step (B).The stopping criterion depends strongly on objectives such as accuracyor speed.

[0215] This algorithm differs from standard methods such as thetrapezoidal rule which is based on equally distributed points in [0, 1]in that there is no relationship between consecutive sets x_(i)(n)=i/nand x_(i)(n)=i/(n+1). In other words, if the approximation given inequation (4) fails a design goal, a complete recalculation of numerousf-values is necessary. On the other hand, it is well known that thetrapezoidal rule gives a 1/n² rate of convergence for a given continuousfunction f.

[0216] Obviously, the quality of the trapezoidal rule is based on ahighly homogeneous set of points. To quantify the homogeneity of afinite set of points, the definition of a discrepancy of a given set wasintroduced: $\begin{matrix}{{D(x)} = {\sup\limits_{R}{{{m(R)} - {p(R)}}}}} & (5)\end{matrix}$

[0217] Here, R runs over all rectangles [0, r] with 0≦r≦1, m(R) standsfor the length r of the closed interval R, and p(R) is the ratio of thenumber of points of X in R and the number of all points of X. Thedefinition given in equation (5) can be generalized to the case of ddimensions (d=2, 3, . . . ), where the term interval must be interpretedas a d dimensional rectangle. The lower the discrepancy the better ormore homogeneous the distribution of the set. The discrepancy of aninfinite sequence X={x₁, x₂, . . . , x_(n), . . . } is a new sequence ofpositive real numbers D(X_(n)), where X_(n) stands for the first nelements of X. Other definitions for the discrepancy of a set exist thatavoid the worst-case scenario according to (5).

[0218] There exists a set of points of given length that realizes thelowest discrepancy. It is well known in the art that the followinginequality (6) holds true for all finite sequences X of length n in thed dimensional unit cube (the Roth bound, see Weisstein [1999] or Kocisand Whiten [1997]): $\begin{matrix}{{D(X)} \leq {B_{d}\quad {\frac{\quad ( {\log \quad n} )^{{({d - 1})}/2}}{n}.}}} & (6)\end{matrix}$

[0219] B_(d) depends only on d. Except for the trivial case d=1, it isnot known whether the theoretical lower bound is attainable. Manyschemes to build finite sequences Xof length n do exist that deliver aslightly worse limit $\begin{matrix}{{D(X)} \leq {B_{d}\quad {\frac{( {\log \quad n} )^{d}}{n}.}}} & (7)\end{matrix}$

[0220] There are also infinite sequences X with $\begin{matrix}{{D( X_{n} )} \leq {B_{d}\quad \frac{( {\log \quad n} )^{d}}{n}}} & (8)\end{matrix}$

[0221] for all for all sub-sequences consisting of the first n elements.The latter result gave rise to the definition of the Low Discrepancy(infinite) Sequences X. The inequality in equation (8) must be valid forall sub-sequences of the first n elements, where B_(d) is anappropriately chosen constant. Low Discrepancy Sequences are also knownas quasi-random sequences.

[0222] Many of the well-studied low-discrepancy sequences ind-dimensional unit cubes can be constructed as combinations of1-dimensional low-discrepancy sequences. The most popularlow-discrepancy sequences are based on schemes introduced by Corput[1937], Halton [1960], Sobol' [1967], and Niederreiter [1992].

[0223] The relationship between integrals, approximations, and aninfinite sequence X={x₁, x₂, . . . } in n dimensions is given by theKoksma-Hlawka inequality. $\begin{matrix}{{{{{I(f)} - {I_{n}(f)}}} \leq {{V(f)} \cdot {D( X_{n} )}}}{{I(f)} = {{\int_{0}}^{1}{{{xf}(x)}}}}{{I_{n}(f)} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\quad x_{n}}}}} & (9)\end{matrix}$

[0224] where V(i) is the variation of f in the sense of Hardy andKrause. For more information regarding Low Discrepancy Sequences, thebook “Random Number Generation and Quasi-Monte Carlo Methods”, by H.Niederreiter, CBMS-NSF Regional Conference Series in Applied Math.,No.63, SIAM, 1992 gives a comprehensive introduction into theimplementation of Low Discrepancy Sequences (Halton and Sobol'). TheHalton method is described in detail below. All of the test resultspresented are based on Halton sequences.

[0225] Halton sequences in 1-d start with the choice of a natural numbergreater than 1. Though not absolutely necessary, prime numbers p=2, 3,5, . . . are typically chosen. If p is a given prime number and x_(n)the n^(th) element of the Halton sequence, the following algorithmdetermines x_(n).

[0226] (A) write n down in the p-ary system

n=n _(q) . . . n ₀ , n=n ₀ +n ₁ ·p+ . . . +n _(q) ·p ^(q)

[0227] (B) Reverse the order of the digits and add the p-ary point

0.n₀n₁. . . n_(q)

[0228] (C) It is

x _(n) =n ₀ ·p ⁻¹ +n ₁ p ⁻² + . . . +n _(q) p ^(−(q+1))

[0229] The n^(th) element of the Halton sequence can be calculatedindependently of all other elements. As mentioned above, in d dimensionsone has to interpret different 1-dimensional Halton sequences ascoordinates of points in d dimensions. It is very common to start withthe first d prime numbers.

[0230]FIG. 8A shows the first 1000 elements of a Halton sequence (left)in the unit square for a valid choice of starting prime numbers, namely(2, 3). The graph on the right hand side is constructed with the aid ofuniformly distributed random numbers. The differences are pronounced, asmay be seen.

[0231] Halton sequences in 1-D are Low Discrepancy Sets in the sense ofequation (8). More precisely, for all n and for all Halton sequences Xthat are based on a prime number p: $\begin{matrix}{{{D(X)} \leq {C_{p}\frac{\log \quad n}{n}\quad {with}}}\text{}{C_{p} = \{ \begin{matrix}\frac{p^{2}}{4( {p + 1} )\log \quad p} & {{when}\quad p\quad {is}\quad {even}} \\\frac{p - 1}{4\quad \log \quad p} & {{when}\quad p\quad {is}\quad {odd}}\end{matrix} }} & (10)\end{matrix}$

[0232] A similar result holds true for Halton sequences in d dimensionalunit squares. In a 2-dimensional unit square for the (p,q) Haltonsequence with prime numbers p and q the discrepancy is $\begin{matrix}{{D(X)} \leq {\frac{2}{n} + {\frac{( {\log \quad n} )^{2}}{n}\lbrack {( {\frac{p - 1}{2\quad \log \quad p} + \frac{p + 1}{2\quad \log \quad n}} )( {\frac{q - 1}{2\quad \log \quad q} + \frac{q + 1}{2\quad \log \quad n}} )} \rbrack}}} & (11)\end{matrix}$

[0233] Scanning Methods Based on Low Discrepancy Sequences inconjunction with TSP

[0234] Assume an open convex region O in 2d. Furthermore, assume a LowDiscrepancy Sequence in this region O. If it is known that any point ofa small object in O is in the neighborhood of at least one of the firstN Low Discrepancy points of the given sequence and if the size of thisneighborhood is reasonably small, the following algorithm can be used toimplement an efficient scanning scheme for this situation.

[0235] A) Fix one of the points as the starting point.

[0236] B) Construct a good approximation of the TSP (Traveling SalesmanPath), e.g. Lin's nearest neighbor algorithm.

[0237] C) If necessary try to increase the efficiency of the solution bymanual inspection.

[0238]FIG. 8B—Splining Low Discrepancy Sequence Path Segments

[0239] In some cases there may be good reasons to fix the starting pointbased on additional information, e.g. probability distribution of thelocation of the small object. Furthermore, the constructed path liesentirely in O, i.e. it cannot leave the convex region O. Usually, thepoint (C) is of minor concern. Because of distribution properties of thegiven Low Discrepancy Sequence in S, it is likely that neighborhoods arenot exhausted during an early phase of the execution of the algorithm.It is also possible to spline the resulting piecewise linear trajectoryto guarantee better curvature behavior or to modify Lin's algorithm insuch a manner that the newly chosen line and the old one produce“curvatures” below a desired value (or at least smoother transitions).Naturally, this will in almost all cases increase the length of thecomputed trajectory.

[0240]FIG. 8B depicts the result of such an optimization routine. Theright graph is simply a splined version of the left one.

[0241]FIG. 9—Flowchart of Low Discrepancy Sequence Scanning Process

[0242]FIG. 9 is a flowchart of a Low Discrepancy Sequence scanningprocess. Low Discrepancy Sequences are described above with reference toFIGS. 8A and 8B. It should be noted that in some embodiments some of thesteps presented below may occur in a different order than shown, or maybe omitted.

[0243] As FIG. 9 shows, in 902, a Low Discrepancy Sequence may becalculated for the scan region. In one embodiment, the calculation ofthe Low Discrepancy Sequence may be performed in a preprocessing phasebefore an object is placed in the scan region. In one embodiment,calculating the Low Discrepancy Sequence of points in the region maycomprise determining a plurality of points, wherein any location in theregion is within a specified distance of at least one of the LowDiscrepancy Sequence of points.

[0244] In 904, a motion control trajectory may be generated based on thecalculated Low Discrepancy Sequence of 902. In one embodiment,generating the motion control trajectory may be performed by generatinga Traveling Salesman Path (TSP) from the Low Discrepancy Sequence ofpoints, where the TSP includes each point of the Low DiscrepancySequence of points, and re-sampling the TSP to produce a sequence ofmotion control points comprising the motion control trajectory.

[0245] In one embodiment, generating the Traveling Salesman Path isperformed by applying Lin's Nearest Neighbor algorithm to the LowDiscrepancy Sequence of points to generate the Traveling Salesman Path.

[0246] In one embodiment, the generated TSP comprises a first sequenceof points which defines a first trajectory having a first maximumcurvature. Re-sampling the TSP comprises manipulating the first sequenceof points to produce the sequence of motion control points defining asecond trajectory having a second maximum curvature which is less thanthe first maximum curvature. Thus, the TSP sequence of points may beused to generate a scan curve with a lower maximum curvature than thefirst trajectory. In one embodiment, the sequence of motion controlpoints may be a superset of the first sequence of points. In anotherembodiment, the sequence of motion control points may comprise a subsetof the first sequence of points and one or more additional points.

[0247] In 906, the region may be scanned using the generated motioncontrol trajectory. In one embodiment, scanning the region using thegenerated motion control trajectory may comprise scanning the regionalong the motion control trajectory, i.e., measuring the region at aplurality of points along the generated motion control trajectory.

[0248] In one embodiment the object to be scanned may be placed in thescan region prior to said scanning. In one embodiment, the object mayappear in the scan region automatically, such as in an inspection systemwhere the object is moved on a conveyor past the scanning apparatus. Theobject may be paused in front of the scanner, or may simply be movedpast slowly enough that the scanning operation may be performed withoutstopping the object's motion. In one embodiment, the presence of theobject in the scan region may be undetermined, i.e., the region may bescanned to determine whether the object is there.

[0249] It should be noted that although the example given was for a twodimensional region and object, the method described above may be appliedto regions and objects of one or two dimensions. In other embodiments,the method may be applied to regions and objects of dimensionalitygreater than two.

[0250] In 908, one or more characteristics of the object may bedetermined in response to the scanning of 906. Examples of suchcharacteristics may include the object's precise location, shape,colors, identity, or any other detectable characteristic. In oneembodiment, measuring the region at the plurality of points along theconformal scanning curve produces data which may be examined or analyzedto determine the one or more characteristics of the object.

[0251] Finally, in 910, output indicating the one or morecharacteristics of the object may be generated. In one embodiment, theoutput may be displayed on a computer display. In another embodiment,the output may be transmitted to one or more internal or externalsystems. In another embodiment, the output may be stored in a computermemory for later analysis or use.

[0252] Low Discrepancy Curves in the Unit Square

[0253] The following theorem is a result in ergodic dynamics. Letx_(n)={nα} and X_(n)={x₁, . . . , x_(n)}, where α=(α₁. . . , α_(d)) isirrational and α₁, . . . , α_(d) are linearly independent over therationals. Then for almost all α in R^(d) (i.e. with exception of a setof points that has measure 0): $\begin{matrix}{{D( X_{n} )} = {O( \frac{\log^{d + 1 + ɛ}n}{n} )}} & (12)\end{matrix}$

[0254] Given a piecewise smooth and finite curve C in the unit square S,let R be an arbitrary aligned rectangle in S with lower left corner in(0,0). Furthermore, let L be the length of the given curve in S and l bethe length of the sub-curve of C that lies in R. In the case ofwell-distributed curves, the ratio l/L should represent the area A(R) ofR. This gives rise to the following definition of discrepancy of a givenfinite piecewise smooth curve in S: $\begin{matrix}{{D(C)} = {\sup\limits_{R}{{\frac{l}{L} - {A(R)}}}}} & (13)\end{matrix}$

[0255] It would be desirable to construct curves C with the propertythat the discrepancy is always small. More precisely, an infinite andpiecewise sufficiently smooth curve C: R⁺−>S in natural parameterizationmay be termed a low-discrepancy curve if for all positive arc lengths Lthe curves C_(L)=C/_([0,L]) satisfies the inequality (the function Fmust be defined appropriately):

D(C _(L))≦F(L)  (14)

[0256] A piecewise smooth curve in natural parameterization generatessequences {x₁, x₂, . . . , x_(n), . . . }by

[0257] x_(n)=C(nΔ) where Δ is a fixed number.

[0258] The inequality (14) implies a similar formula for the derivedsequence {x₁, x₂, . . . , x_(n), . . . }. In other words, alow-discrepancy curve generates many sequences that show low-discrepancylike behavior from the sequence standpoint. Because of (12), a realisticgoal is $\begin{matrix}{{F(L)} = {{{O( \frac{\log^{2 + ɛ}\quad L}{L} )}\quad {with}\quad d} = 2}} & (15)\end{matrix}$

[0259] which may be shown to be attainable.

[0260] To this end, let for α=(α₁, α₂) C_(a)(α) be the piecewise linearcurve (tα₁ mod 1,tα₂ mod 1)=({nα₁},{nα₂}) where t is in R⁺ (see FIG.11A).

[0261] Theorem 4:

[0262] For almost all numbers α in R² C_(α)(α) is a low-discrepancycurve in the sense of (14) and (15).

[0263] Proof:

[0264] Without loss of generality, it is assumed that α₁,α₂>0. C_(a)(α)has common points with axes x=0 and y=0 for {nα₂/α₁} and {nα₁/α₂},respectively. n is an arbitrary natural number. For almost all α₁ and α₂all three numbers (α₁,α₂), α₁/α₂, and α₂/α₁ generate low-discrepancysequences in the sense of (12) in R², R¹, and R¹, respectively.

[0265] Let tan(φ)=α₂/α₁. Let [0,a]×[0,b] be a rectangle with 0<a, b<1.Without loss of generality, b/a<tan(φ) may be assumed. See FIG. 10 forfurther details and for illustrative definitions of I₁, I₂, and I₃.Mathematically, they may be defined as: $\begin{matrix}{{I_{1} = \frac{a^{2}}{2\quad {\cos (\varphi)}}}{I_{2} = \frac{a( {b - {a\quad {\tan (\varphi)}}} )}{\cos (\varphi)}}{I_{3} = \frac{a^{2}\quad {\sin (\varphi)}}{2\quad {\cos^{2}(\varphi)}}}{{{I_{1}\quad {\sin (\varphi)}} + {( {I_{2} + I_{3}} ){\cos (\varphi)}}} = {ab}}} & (16)\end{matrix}$

[0266] The latter term stands for the percentage that an average line in[0,1]×[0,1]with slope tan(φ) has in common with the rectangle[0,a]×[0,b]. It is exactly the area of this rectangle.

[0267] The Koskma-Hlawka inequality (4) can be applied to approximateintegrals I₁, I₂, I₃. In case of I₁ define:${l_{1}(x)} = \frac{a - x}{\cos (\varphi)}$

[0268] for 0≦x≦a and ₁(x)=0 for a≦x≦1

[0269] It follows that $\begin{matrix}{{{I_{1} - {\sum{l_{1}( x_{n} )}}}} = {{{O( \frac{\log^{2 + ɛ}\quad n}{n} )}\quad {where}\quad x_{n}} = \{ {n\quad {\alpha_{1}/\alpha_{2}}} \}}} & (17)\end{matrix}$

[0270] where the sum stands for the length of that part of the givencurve that lies in [0,a]×[0,b]. I₂ and I₃ can be treated similarity.(16) and (17) combined guarantee that C_(a)(α) is low-discrepancy.

[0271] q.e.d.

[0272] FIGS. 11A-11C—Low Discrepancy (Straight Line) Scan Paths on theUnit Square In the following, two variations of Theorem 4 are needed. AsFIGS. 11A-11C demonstrate, these two new variations (11B) and (11C) arebased on total reflections of (tα₁, tα₂) at the upper and lower edges ofthe unit square and at all four edges of the unit square, respectively.The resulting curves are referred to as C_(b)(α) and C_(c)(α). Thelatter curve is continuous.

[0273] Theorem 5:

[0274] For almost all numbers α in R² C_(b)(α) and C_(c)(α) arelow-discrepancy curves in the sense of (14) and (15).

[0275] Proof:

[0276] One can show that Theorem 4 is still valid when in the definitionof low-discrepancy curves a much broader class of rectangles R isconsidered. More precisely, one can replace rectangles R=[0,a]×[0,b]with the more generic family R=[c,a]×[d,b].

[0277] Curves of Type (b):

[0278] Such a curve can be translated into an equivalent version actingin [0,1]×[0,2]. To this end, reflections at the upper edge (see FIG.11B) are ignored. What results is an equivalent scheme of type (a) in[0,1]×[0,2]. For almost all choices of a the resulting curve in[0,1]×[0,2] is low-discrepancy. The relation between the original spaceand the new one is straightforward. The original curve goes through arectangle R=[0,a]×[0,b] if and only if the derived curve in [0,1]×[0,2]goes through [0,a]×[0,b] or through [0,a]×[2−b,2] (see the remark at thebeginning of this proof). The latter means that C_(b)(α) satisfies (14)and (15).

[0279] Curves of Type (c):

[0280] Essentially the arguments for type (b) are valid, for almost allα curves of type (b) in [0,2]×[0,1] are low-discrepancy. Such curves(see FIG. 11C) can be generated when reflections at the right edge areignored. The mirrored version of this curve goes through a rectangleR=[0,a]×[0,b] if and only if the original curve in [0,2]×[0,1] goesthrough [0,a]×[0,b] or [2−a,2]×[0,b] (see the remark at the beginning ofthis proof). The latter means that C_(c)(α) satisfies (14) and (15).

[0281] q.e.d.

[0282] Curves C_(c)(α) can be regarded as first examples of continuoustrajectories in a unit square that offer low-discrepancy behavior. Inreal scanning scenarios they are highly efficient compared to othertechniques.

[0283] The following results are also relevant. The proofs are similarto those of Theorem 4 and Theorem 5.

[0284] Theorem 6:

[0285] For almost all numbers α in R^(d) C_(a)(α), C_(b)(α) and C_(c)(α)are low-discrepancy curves in the sense of (14) and (15) ind-dimensional unit cubes.

[0286] FIGS. 12A and 12B—Flowcharts of a Method for Generating a LowDiscrepancy Curve in the Unit Square

[0287]FIGS. 12A and 12B are flowcharts of a method for generating acurve in a region, such as a unit square, in accordance with the theorypresented above, and in relation to the illustrations of FIGS. 11A-11C.FIG. 12A describes the method at a high level, and FIG. 12B flowcharts adetailed embodiment of the method. In one embodiment, the generatedcurve may be a Low Discrepancy Curve. It should be noted that in variousembodiments, one or more of the steps may be performed in a differentorder than shown, or may be omitted as desired. Furthermore, additionalsteps may be performed as desired. It should also be noted that in thepreferred embodiment, the unit square is used as described below, butthat in other embodiments, other geometries may be used, includingrectangles, and higher dimensional regions, such as n-dimensional unitcubes and rectangles, among others.

[0288]FIG. 12A—Flowcharts of a Method for Generating a Curve in a Region

[0289]FIG. 12A flowcharts one embodiment of a method for generating acurve in a region. As FIG. 12A shows, in 1222, an unbounded LowDiscrepancy Point may be generated. Note that as used herein, the term“unbounded Low Discrepancy Point” refers to a generated Low DiscrepancyPoint which may or may not fall within the bounds of the region.

[0290] In 1224, one or more boundary conditions may be applied to theunbounded Low Discrepancy Point to generate a bounded Low DiscrepancyPoint, i.e., a Low Discrepancy Point which is located within the region.It should be noted that applying the one or more boundary conditions tothe unbounded Low Discrepancy Point may be performed in various ways.For example, in one embodiment, the position of the unbounded LowDiscrepancy Point may be checked for inclusion in the region, and, iffound to be outside the region, the one or more boundary conditions maybe applied to the point. Thus, from this perspective, the one or moreboundary conditions may be applied only to unbounded Low DiscrepancyPoints which fall outside the region. In another embodiment, theapplication of the one or more boundary conditions may include checkingthe position of the unbounded Low Discrepancy Point, and modifying theposition of the point if found to be outside the region. Thus, from thisperspective, the boundary conditions may be considered to be applied toall the generated unbounded Low Discrepancy Points, although possibly ona subset of the points may be re-positioned, as the boundary conditionsrequire.

[0291] As FIG. 12A indicates, in 1226 if the Low Discrepancy Pointsgeneration process is not complete, then the method repeats steps 1222and 1224, i.e., repeating the generating and applying one or moreboundary conditions, one or more times until a stopping condition ismet, thereby generating a Low Discrepancy Sequence in the region.

[0292] If there are no more points to be generated, then in 1228 the LowDiscrepancy Sequence may be stored. The generated Low DiscrepancySequence may represent the curve in the region. In the preferredembodiment, the curve may comprise a Low Discrepancy Curve in theregion. It should be noted that in various embodiments, the curverepresented by the Low Discrepancy Sequence may pass through each pointin the Low Discrepancy Sequence, may pass through or close to one ormore of the points, or may pass substantially close to one or more ofthe points.

[0293] Finally, in 1230, output may be generated comprising the LowDiscrepancy Sequence, wherein the Low Discrepancy Sequence defines thecurve in the region. In one embodiment, generating the output mayinclude displaying the output on a computer display. In otherembodiments, generating the output may include printing the output, ortransmitting the output to an external system.

[0294]FIG. 12B—Flowcharts of a Method for Generating a Low DiscrepancyCurve in the Unit Square

[0295]FIG. 12B flowcharts one embodiment of the method of 12A in detail.As FIG. 12A shows, in 1202 a pair of irrational numbers (α₁,α₂) may beselected such that a sequence ({(n*α₁)mod1},{(n*α₂)mod1}) is a LowDiscrepancy Sequence (LDS) in the unit square, where n ranges over thenatural numbers. In other words, integral multiples of irrational valuesα may provide successive coordinate values in the unit square. Note thatthe modulo operation prevents any of the coordinate values from leavingthe square. In the preferred embodiment, (α₁,α₂) are selected such thatthey are not integral multiples of one another. It should be noted thatin other embodiments, the Low Discrepancy Sequence may be defined onhigher dimensional surfaces and spaces, in which case, rather than apair of irrational values, tuples of more than two values may bedefined, e.g., (α₁,α₁, . . . , α_(n)), which give rise to a higherdimensional LDS. It should also be noted that although the ability togenerate the Low Discrepancy Sequence mentioned above may be a conditionfor selection of the pair of irrational numbers (α₁,α₂), the actualgeneration of the LDS is not required.

[0296] In 1204, a maximum length L and a step rate ε(epsilon) may beselected for use in guiding the construction of a Low Discrepancy Curve(LDC) in the unit square from the pair of irrational numbers (α₁, α₂)described in 1202. In effect, rather than using integral multiples ofthe values, α_(i), multiples of the product ε*α_(i) may be used. Notethat as ±approaches 0, the sequence of points generated approaches aline. This line is the generated LDC.

[0297] In 1206 one or more operational variables may be initialized. Forexample, a current length of the LDC, 1, may be initialized to zero. Aninitial position vector may be selected, and a current position vector(x, y) initialized to the initial position. For example, the currentposition vector (x, y) may be initialized to (0,0). In otherembodiments, the initial position vector may be initialized to a randomposition in the unit square, or to any other determined position in theunit square, as desired.

[0298] In 1208, the current position vector (x, y) coordinate values maybe incremented by (εα₁, εα₂), respectively, and boundary conditionsalong each axis applied to keep the values inside the unit square. Inone embodiment, the boundary conditions may include toroidal conditionsat opposite borders of the square, such that a value which crosses aborder ‘wraps’ around and ‘re-enters’ the square from the opposite side.In another embodiment, the boundary conditions may include reflectanceat the borders, such that when a value crosses a border, the value is‘reflected’ back from the border into the square. In other embodiments,combinations of these boundary conditions may be applied to the variousborders of the unit square. In further embodiments, other boundaryconditions may be applied as desired. In one embodiment, after thecoordinate values (x, y) are incremented, the current length, 1, may bedetermined by adding the distance from the previous current position tothe incremented current position, using the standard distancecalculation.

[0299] In 1210, a stopping condition may be checked to determine whetherto stop iterating. In the preferred embodiment, the current length, l,may be compared to the length L selected in 1204, and if l is determinedto be less than L, then the iteration may continue, repeating step 1208until the stopping condition is met. In other words, successivecoordinate values (Low Discrepancy Sequence points) may be generateduntil the length of the generated curve meets or exceeds the maximumlength L. It is also contemplated that other stopping conditions may beused as desired, e.g., when the number of points generated exceeds somethreshold. In one embodiment, the incremented coordinate values (x, y)generated in an iteration may comprise a Low Discrepancy Sequence point,which, when used with other Low Discrepancy Sequence points generated inother iterations of the process, may be used to generate the LowDiscrepancy Curve of 1204.

[0300] If the stopping condition is met, e.g., if l meets or exceeds L,then the iteration may be stopped, and in 1212 the generated LowDiscrepancy Sequence representing the Low Discrepancy Curve may beoutput. In one embodiment, the generated output may be stored for lateruse. In one embodiment, the generated output may be displayed on adisplay device such as a computer monitor screen or a printer.

[0301] In one embodiment, the Low Discrepancy Curve represented by thegenerated Low Discrepancy Sequence may be used as a scan path in ascanning application.

[0302]FIG. 13A—Low Discrepancy Curves

[0303] Another approach to achieve the relationship (13) may be based onother Low Discrepancy Sequences. The sequence {(x₁,y₁), (x₂,y₁),(x₂,y₂), . . . , (x_(n),y_(n)), (x_(n+1),y_(n)), (x_(n+1),y_(n+1)), . .. } can be regarded as the endpoints of lines that are concatenated.FIG. 13A illustrates a first part of a Low Discrepancy Curve based on aspecific Halton sequence in 2d. More specifically, FIG. 13A shows thefirst part of the resulting curve where Halton sequences with primenumbers 2 and 3 are used. The curve fills the space in the sense thatthe curve comes arbitrarily close to any point infinitely often. It isimportant to note that other strategies based on the Halton set as wellas other Low Discrepancy Sequences may exist.

[0304]FIG. 13B— Splined Low Discrepancy Curves

[0305]FIG. 13B illustrates a scanning scheme based on a splined LowDiscrepancy Curve. This scheme is based on the Low Discrepancy Curvescheme described above with reference to FIG. 13A, but splines theorthogonal segments together to substantially reduce the maximumcurvature of the path. In other words, the ninety degree turns of theoriginal Low Discrepancy Curve path may be replaced with smooth splines,resulting in an efficient low curvature path which substantially coversthe space in its entirety. Further details of Splined Low DiscrepancyCurve scanning schemes are presented below with reference to FIG. 18.

[0306]FIG. 13C—Comparison of Conformal Spiral and Low DiscrepancySearching Travel Distances

[0307] Structured systematic methods, such as the Archimedes spiral orthe Boustrophedon path represent a class of search strategies. In suchstrategies, the search distance is predictable given a spot location,because of the systematic nature of the algorithms. Typically suchtrajectories start either at the center of the search area (Spiralsearches) or at a corner (Boustrophedon), and thus the searching timesincrease the further along the curve from the starting point the spot islocated.

[0308] On the other hand, unstructured but systematic searches, such asproposed by the Low Discrepancy strategy, perform arbitrary moves aroundthe space, attempting to cover the whole search space in a uniformfashion. Such strategies form a second class of search methods.

[0309] Rather than comparing each of the many methods presented herein,the Conformal Spiral was chosen to represent the first class ofapproaches and the Low Discrepancy Curve based search to represent thesecond class. Comparisons among methods in the first class can be donesimply in terms of the curve length (straightforward computation)required to arrive in a certain location in space. On the other hand, acomparison between classes requires a simulated environment.

[0310] To compare the performances of the different approaches, a searchwas run several times (1000) for each coarse search technique and thetravel distance required to find the initial coarse location of the spotwas recorded. The spot was placed randomly (uniform distribution) insidethe square. FIG. 13C presents the distribution of such distances for theLow Discrepancy Curve based model, as well as for the Conformal Spiralcurve based model. Note that the Low Discrepancy Curve method has adistribution with less variance but with a long tail as compared to theConformal Spiral method.

[0311]FIG. 13D—Comparison of Conformal Spiral and Low DiscrepancySearching Position Bias

[0312]FIG. 13D compares the search path length bias of the ConformalSpiral approach with the Low Discrepancy Curve based approach. Thedistance of the located spot from the center of the search area versusthe search distance required to find the spot for a single run isplotted. The distance from the center was chosen because the ConformalSpiral starts its search in the center and progresses towards the searcharea boundaries. FIG. 13D shows a very interesting property of the lowdiscrepancy search: regardless of the location of the point, the searchdistance traveled is approximately the same, and close to the average.This property makes the Low Discrepancy Curve based strategy veryattractive for industrial applications where, ideally, the processingtime for every task should be similar. On the other hand, theperformance of the Conformal Spiral becomes progressively worse as thespot is located further from the center.

[0313] In applications where a rough estimate for the size of the spotis unavailable, the Low Discrepancy searching approach may be the onlyviable solution. A Conformal Spiral without enough turns could miss thespot completely, and a new search would have to be started. In the LowDiscrepancy approach, such phenomena are avoided due to the balancednature of the search path. The Low Discrepancy approach allows thesearching distances to be almost the same regardless of the spotlocation.

[0314] The final stage of the search strategy and simulation resultsregarding the accuracy of the spot location are presented below.

[0315]FIG. 14—Flowchart of Low Discrepancy Curve Scanning Process

[0316]FIG. 14 is a flowchart of a Low Discrepancy Curve scanningprocess. Low Discrepancy Sequences and Curves are described above withreference to FIGS. 8-13. It should be noted that in some embodimentssome of the steps presented below may occur in a different order thanshown, or may be omitted.

[0317] As FIG. 14 shows, in 1402, a Low Discrepancy Sequence may becalculated for the scan region. In one embodiment, calculating the LowDiscrepancy Sequence in the region may comprise determining a pluralityof points, wherein any location in the region is within a specifieddistance of at least one of the Low Discrepancy Sequence of points.

[0318] In 1404, a Low Discrepancy Curve may be generated based on thecalculated Low Discrepancy Sequence of 1402. In one embodiment,generating the Low Discrepancy Curve may comprise using each successivepair of the Low Discrepancy Sequence of points to determine a pluralityof orthogonal line segments which connect the pair of points, andre-sampling the plurality of orthogonal line segments to generate a LowDiscrepancy Curve segment. A contiguous sequence of the Low DiscrepancyCurve segments calculated from the successive pairs of the LowDiscrepancy Sequence of points may then comprise the Low DiscrepancyCurve, i.e., the Low Discrepancy Curve segments corresponding to thesuccessive pairs of the Low Discrepancy Sequence of points may besequentially connected to form the Low Discrepancy Curve.

[0319] In one embodiment, re-sampling the plurality of orthogonal linesegments may include fitting a curve to a plurality of points comprisedin the plurality of orthogonal line segments subject to one or moreconstraints, such as curvature limits, and generating a second pluralityof points along the fit curve, wherein the second plurality of pointsdefine the Low Discrepancy Curve segment.

[0320] In one embodiment, the region may be defined by a coordinatespace having a plurality of orthogonal axes, wherein each of theplurality of orthogonal axes corresponds respectively to a dimension ofthe region. Each of the pair of points may have a plurality ofcoordinates corresponding respectively to the plurality of orthogonalaxes. Each of the plurality of orthogonal line segments is parallel to arespective one of the orthogonal axes, and has a first endpoint and asecond endpoint, wherein the first endpoint has a first plurality ofcoordinates, wherein the second endpoint has a second plurality ofcoordinates, and wherein the first plurality of coordinates and thesecond plurality of coordinates differ only in value of a coordinatecorresponding to a respective one of the plurality of orthogonal axes.In this manner, the plurality of orthogonal line segments which connectthe pair of points comprises a contiguous sequence of said line segmentscorresponding to a specified order of the plurality of orthogonal axes.The re-sampling the plurality of orthogonal line segments to generate aLow Discrepancy Curve segment then comprises re-sampling the contiguoussequence of said line segments in the specified order to generate theLow Discrepancy Curve segment.

[0321] 2D Example:

[0322] An example of the process described above for the 2-dimensionalcase follows:

[0323] In one embodiment, the plurality of orthogonal axes may comprisean x axis and a y axis, i.e., the region has a dimensionality of two,and the plurality of line segments comprises two orthogonal linesegments including a first line segment and a second line segment. Inthis case, a first of the pair of points has two coordinates, (x0, y0),corresponding respectively to the x and y axes, and a second of the pairof points has two coordinates, (x1, y1), also corresponding respectivelyto the x and y axes. Each of the line segments may have a first endpointand a second endpoint, where the second endpoint of the first linesegment is equal to the first endpoint of the second line segment. Thetwo orthogonal line segments which connect the pair of points maytherefore comprise a contiguous sequence of said line segments in thespecified order, and the two orthogonal line segments may be re-sampledto generate a Low Discrepancy Curve segment by re-sampling thecontiguous sequence of said line segments in the specified order.

[0324] If the specified order of the plurality of orthogonal axes is (x,y), then the first endpoint of a first of the two line segments hascoordinates (x0, y0), and the second endpoint of the first of the twoline segments has coordinates (x1, y0);

[0325] the first endpoint of a second of the two line segments hascoordinates (x1, y0), and the second endpoint of the second of the twoline segments has coordinates (x1, y1). Thus, the two line segmentscomprise a contiguous path between the pair of points.

[0326] If the specified order of the plurality of orthogonal axes is (y,x), then the first endpoint of a first of the two line segments hascoordinates (x0, y0), and the second endpoint of the first of the twoline segments has coordinates (x0, y1);

[0327] the first endpoint of a second of the two line segments hascoordinates (x0, y1), and the second endpoint of the second of the twoline segments has coordinates (x1, y1). Thus, the two line segmentscomprise another contiguous path between the pair of points.

[0328] 3D Example:

[0329] An example of the process described above for the 3-dimensionalcase follows:

[0330] In one embodiment, the plurality of orthogonal axes comprises anx axis, a y axis, and a z axis, i.e., the region has a dimensionality ofthree, and the plurality of line segments comprises three orthogonalline segments including a first line segment, a second line segment, anda third line segment. In this case, a first of the pair of points hasthree coordinates, (x0, y0, z0), corresponding respectively to the x, y,and z axes, and a second of the pair of points has three coordinates,(x1, y1), also corresponding respectively to the x, y, and z axes.

[0331] Each of the line segments may have a first endpoint and a secondendpoint, where the second endpoint of the first line segment is equalto the first endpoint of the second line segment, and where the secondendpoint of the second line segment is equal to the first endpoint ofthe third line segment. Thus, said three orthogonal line segments whichconnect the pair of points may comprise a contiguous sequence of saidline segments in the specified order, and the three orthogonal linesegments may be re-sampled to generate a Low Discrepancy Curve segmentby re-sampling the contiguous sequence of line segments in the specifiedorder.

[0332] If the specified order of the plurality of orthogonal axes is (x,y, z), then the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x1, y0, z0);

[0333] the first endpoint of a second of the three line segments hascoordinates (x1, y0, z0), and the second endpoint of the second of thethree line segments has coordinates (x1, y1, z0); and

[0334] the first endpoint of a third of the three line segments hascoordinates (x1, y1, z0), and the second endpoint of the third of thethree line segments has coordinates (x1, y1, z1). Thus, the three linesegments comprise a contiguous path between the pair of points.

[0335] If the specified order of the plurality of orthogonal axes is (x,z, y), then the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x1, y0, z0);

[0336] wherein the first endpoint of a second of the three line segmentshas coordinates (x1, y0, z0), and the second endpoint of the second ofthe three line segments has coordinates (x1, y0, z1); and

[0337] wherein the first endpoint of a third of the three line segmentshas coordinates (x1, y0, z1), and the second endpoint of the third ofthe three line segments has coordinates (x1, y1, z1). Thus, the threeline segments comprise another contiguous path between the pair ofpoints.

[0338] If the specified order of the plurality of orthogonal axes is (y,z, x), then the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x0, y1, z0);

[0339] the first endpoint of a second of the three line segments hascoordinates (x0, y1, z0), and the second endpoint of the second of thethree line segments has coordinates (x0, y1, z1); and

[0340] the first endpoint of a third of the three line segments hascoordinates (x0, y1, z1), and the second endpoint of the third of thethree line segments has coordinates (x1, y1, z1). Thus, the three linesegments comprise another contiguous path between the pair of points.

[0341] If the specified order of the plurality of orthogonal axes is (y,x, z), then the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x0, y1, z0);

[0342] the first endpoint of a second of the three line segments hascoordinates (x0, y1, z0), and the second endpoint of the second of thethree line segments has coordinates (x1, y1, z0); and

[0343] the first endpoint of a third of the three line segments hascoordinates (x1, y1, z0), and the second endpoint of the third of thethree line segments has coordinates (x1, y1, z1). Thus, the three linesegments comprise another contiguous path between the pair of points.

[0344] If the specified order of the plurality of orthogonal axes is (z,x, y), then the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x0, y0, z1);

[0345] the first endpoint of a second of the three line segments hascoordinates (x0, y0, z1), and the second endpoint of the second of thethree line segments has coordinates (x1, y0, z1); and

[0346] the first endpoint of a third of the three line segments hascoordinates (x1, y0, z1), and the second endpoint of the third of thethree line segments has coordinates (x1, y1, z1). Thus, the three linesegments comprise another contiguous path between the pair of points.

[0347] If the specified order of the plurality of orthogonal axes is (z,y, x), then the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x0, y0, z1);

[0348] the first endpoint of a second of the three line segments hascoordinates (x0, y0, z1), and wherein the second endpoint of the secondof the three line segments has coordinates (x0, y1, z1); and

[0349] the first endpoint of a third of the three line segments hascoordinates (x0, y1, z1), and the second endpoint of the third of thethree line segments has coordinates (x1, y1, z1). Thus, the three linesegments comprise another contiguous path between the pair of points.

[0350] In one embodiment, the generated plurality of orthogonal linesegments comprises a first sequence of points which defines a firsttrajectory having a first maximum curvature. Re-sampling the orthogonalline segments comprises manipulating the first sequence of points toproduce the sequence of motion control points defining a secondtrajectory (the Low Discrepancy Curve segment) having a second maximumcurvature which is less than the first maximum curvature. Thus, thefirst sequence of points may be used to generate a scan curve segment(the Low Discrepancy Curve segment) with a lower maximum curvature thanthe first trajectory. In one embodiment, the sequence of motion controlpoints may be a superset of the first sequence of points. In anotherembodiment, the sequence of motion control points may comprise a subsetof the first sequence of points and one or more additional points.

[0351] In 1406, the region may be scanned using the calculated LowDiscrepancy Curve. In one embodiment, scanning the region using thecalculated Low Discrepancy Curve may comprise scanning the region alongthe Low Discrepancy Curve, i.e., measuring the region at a plurality ofpoints along the calculated Low Discrepancy Curve.

[0352] In one embodiment, generating a Low Discrepancy Sequence ofpoints on the object may be performed prior to the scanning. In oneembodiment, the calculation of the Low Discrepancy Sequence and the LowDiscrepancy Curve may be performed in a preprocessing phase, e.g.,before an object is placed in the scan region. Said another way, in oneembodiment, one or both of the generating the Low Discrepancy Sequenceof points in the region, and the calculating the Low Discrepancy Curvein the region based on the Low Discrepancy Sequence of points may beperformed offline as a preprocessing operation. The measuring the regionat a plurality of points along the Low Discrepancy Curve may then beperformed in a real time phase of the method.

[0353] In one embodiment, generating the Low Discrepancy Sequence ofpoints on the object and calculating the Low Discrepancy Curve on theobject based on the Low Discrepancy Sequence of points may be performedin real time. In another embodiment, the calculation of the LowDiscrepancy Sequence and the Low Discrepancy Curve may be performed in areal time phase during the scanning process, where, if the scanningprocess is not complete, then an additional one or more Low DiscrepancySequence points and a corresponding additional one or more LowDiscrepancy Curve segments may be calculated in real time. The scan maythen continue, measuring the region along the calculated additional oneor more Low Discrepancy Curve segments. In other words, the LowDiscrepancy Curve may be extended in the real time phase of the scanningprocess, and the scanning may continue, taking measurements along theextended Low Discrepancy Curve.

[0354] More specifically, in one embodiment, scanning the region using aLow Discrepancy Curve scanning scheme may comprise:

[0355] a) generating a first Low Discrepancy Sequence of points in theregion;

[0356] b) calculating a first Low Discrepancy Curve segment in theregion based on the first Low Discrepancy Sequence of points;

[0357] c) scanning a portion of the region along the first LowDiscrepancy Curve segment to identify a characteristic of the object;and,

[0358] if the characteristic of the object is not identified, then:

[0359] d) generating a second Low Discrepancy Sequence of points in theregion based on previous Low Discrepancy Sequence points;

[0360] e) calculating a second Low Discrepancy Curve segment in theregion based on the second Low Discrepancy Sequence of points;

[0361] f) scanning a portion of the region along the second LowDiscrepancy Curve segment to identify a characteristic of the object;

[0362] g) repeating d)-f) one or more times until the characteristic ofthe object is identified or until said one or more times equals athreshold number of times.

[0363] In a preferred embodiment of the above method, a) and b) areperformed offline in the preprocessing phase of the method, and c)-g)are performed in the real time phase of the method.

[0364] In one embodiment, the second Low Discrepancy Sequence of pointsmay include a last point of an immediately previous Low DiscrepancyCurve segment and one or more additional Low Discrepancy Sequencepoints.

[0365] In one embodiment, c) may comprise measuring the region at aplurality of points along the first Low Discrepancy Curve, and f) maycomprise measuring the region at a plurality of points along the secondLow Discrepancy Curve.

[0366] In one embodiment the object to be scanned may be placed in thescan region prior to said scanning, or, said another way, the object mayenter the region prior to scanning the region. In one embodiment, theobject may appear in the scan region automatically, such as in aninspection system where the object is moved on a conveyor past thescanning apparatus. The object may be paused in front of the scanner, ormay simply be moved past slowly enough that the scanning operation maybe performed without stopping the object's motion. In one embodiment,the presence of the object in the scan region may be undetermined, i.e.,the region may be scanned to determine whether the object is there.

[0367] It should be noted that although the example given was for a twodimensional region and object, the method described above may be appliedto regions and objects of one or two dimensions. In other embodiments,the method may be applied to regions and objects of dimensionalitygreater than two.

[0368] In 1408, one or more characteristics of the object may bedetermined in response to the scanning of 1406. Examples of suchcharacteristics may include the object's precise location, shape,colors, identity, or any other detectable characteristic. In oneembodiment, measuring the region at the plurality of points along theconformal scanning curve produces data which may be examined or analyzedto determine the one or more characteristics of the object.

[0369] Finally, in 1410, output indicating the one or morecharacteristics of the object may be generated. In one embodiment, theoutput may be displayed on a computer display. In another embodiment,the output may be transmitted to one or more internal or externalsystems. In another embodiment, the output may be stored in a computermemory for later analysis or use.

[0370] Abstract Surfaces and Riemannian Spaces

[0371] Given an abstract surface S with a Riemannian metric defined for(u,v) in [0,1]², e.g. Gray [1998].

ds ² =E(u,v)du ²+2F(u,v)dudv+G(u,v)dv ²  (18)

[0372] E(u,v), F(u,v), and G(u,v) are differentiable functions in u andv where EG−F² is non-negative. The area element dA is defined by:

dA={square root}{square root over (E(u,v)G(u,v)−F(u,v)²)}du^ dv  (19)

[0373] The function

Ψ(u, v)={square root}{square root over (E(u, v)G(u, v)−F(u, v)²)}  (20)

[0374] is nonnegative in [0,1]2 and Ψ² is differentiable.

[0375] Let α=(α₁,α₂) be a given vector (direction) in R². According to(11) and (12) line and area elements of S for a specific direction(du,dv)=(α₁du,α₂du) are $\begin{matrix}{{\frac{s}{u} = \sqrt{( {{{E( {u,v} )}\alpha_{1}^{2}} + {2{F( {u,v} )}\alpha_{1}\alpha_{2}} + {{G( {u,v} )}\alpha_{2}^{2}}} )}}{\frac{A}{u^{2}} = {\sqrt{{{E( {u,v} )}{G( {u,v} )}} - {F( {u,v} )}^{2}}\alpha_{1}\alpha_{2}}}{{If}\quad {follows}}} & (21) \\{\frac{\frac{s}{u}}{\frac{A}{u^{2}}} = \frac{\sqrt{( {{{E( {u,v} )}\alpha_{1}^{2}} + {2{F( {u,v} )}\alpha_{1}\alpha_{2}} + {{G( {u,v} )}\alpha_{2}^{2}}} )}}{\sqrt{{{E( {u,v} )}{G( {u,v} )}} - {F( {u,v} )}^{2}}\alpha_{1}\alpha_{2}}} & (22)\end{matrix}$

[0376] Definition 1:

[0377] A piecewise smooth curve C. R⁺−>S lying on an abstract surface Sis called low-discrepancy curve based on a vector α=(α₁,α₂), iff

[0378] (i) C is S-filling, i.e. C comes arbitrarily close to any pointof S.

[0379] (ii) There is a parameterization of S where (15) is constant forall (u,v).

[0380] (iii) In any regular point of C the tangent vector is parallel toα=(α₁, α₂).

[0381] The following algorithm is based on Definition 1 and (22).

[0382] Algorithm I:

[0383] (I.1) Find a parameterization of S that satisfies (ii) and (iii).See also Remark 1.

[0384] (I.2) Generate a curve in S based on the image of alow-discrepancy curve in the unit square according to Theorem 5.

[0385] Remark 1:

[0386] The parameterization (ii) is not unique. In all examples anoriginally given natural parameterization is modified with the aid ofu−>h(u) or v−>h(v) replacements where h is a smooth diffeomorphism.

[0387] An abstract d-dimensional metric is defined by $\begin{matrix}{{ds}^{2} = {\sum\limits_{i,{j = 1}}^{}\quad {{g_{ij}( {u_{1},\ldots \quad,u_{d}} )}{u_{i}^{2}}{u_{j}^{2}}}}} & (23)\end{matrix}$

[0388] where the matrix consisting of g_(ij): [0,1]^(d)−>R is alwayssymmetric, differentiable, and positive semi-definite. An embedding ofan abstract space (23) in an m-dimensional Euclidean space is adiffeomorphisms f of the cube [0,1]^(d) with f₁(u₁, . . . , u_(d)),f₂(u₁, . . . u_(d)), . . . , f_(m)(u₁, . . . , u_(d): R^(d)−>R^(m) wherethe Riemannian metric of this embedding is described by (16). Usually,this definition is too restrictive. Instead, local diffeomorphisms(patches) should be used where these patches cover the whole space underconsideration.

[0389] Let α=(α₁,α_(2,). . . , α_(d)) be a given vector (direction) inR^(d). According to (23) line and volume/content elements for a specificdirection (du₁,du₂, . . . , du_(d))=(α₁du, α₂du, . . . , α_(d)du) are$\begin{matrix}{{\frac{s}{u} = \sqrt{\sum\limits_{i,{j = 1}}^{d}{{g_{ij}( {u_{i},\ldots \quad,u_{d}} )}\alpha_{i}\alpha_{j}}}}{\frac{V}{u^{n}} = {\sqrt{d\quad e\quad {t( {g_{ij}( {u_{1},\ldots \quad,u_{d}} )} )}}\alpha_{1}\alpha_{2}\quad \ldots \quad \alpha_{d}}}} & (24) \\{\frac{\frac{s}{u}}{\frac{V}{u^{n}}} = \frac{\sqrt{\sum\limits_{i,{j = 1}}^{d}{{g_{ij}( {u_{i},\ldots \quad,u_{d}} )}\alpha_{i}\alpha_{j}}}}{\sqrt{d\quad e\quad {t( {g_{ij}( {u_{1},\ldots \quad,u_{d}} )} )}}\alpha_{1}\alpha_{2}\quad \ldots \quad \alpha_{d}}} & (25)\end{matrix}$

[0390] Definition 2:

[0391] A piecewise smooth curve C:R⁺−>S in the given Riemannian space Sis called low-discrepancy curve based on a vector α=(α₁, α₂, . . . ,α_(d)), iff

[0392] (i) C is S-filling, i.e. C comes arbitrarily close to any pointof S.

[0393] (ii) There is a parameterization of S where (18) is constant forall (u₁, u₂, . . . , u_(d)).

[0394] (iii) In any regular point of C the tangent vector is parallel toα=(α₁, α₂, . . . , α_(d)).

EXAMPLES:

[0395] FIGS. 15A-15C—Surface Scans with Low Discrepancy Curves

[0396] Theorem 4 and Theorem 5 can be used to construct low-discrepancycurves lying on surfaces, various examples of which are given below.

[0397] Unit Cube

[0398] Given the surface of a unit cube, FIG. 15A shows a tiling of thewhole plane with the following property. Whenever two elements of thetiling have an edge in common the same is true for the correspondingfaces of the unit cube. The tiling follows the torus-scheme underlyingTheorem 4. According to this theorem almost all α=(α₁,α₂) generatecurves lying on the surface of the unit cube that are low-discrepancy inthe sense of

[0399] Definition 1.

[0400] Note that in the example shown in FIG. 15A, some paths possibleon the cube are not possible (with a continuous straight curve) on thetiled surface. For example, faces 3 and 5 of the cube (left) areadjacent, while in the tiled version of the surface (right), they arenot. More generally, it may be seen that on the tiled surface, each facehas three unique neighbors (two of the four neighbors are always thesame), while on the cube, each face has four neighbors. Thus, the tiledsurface accommodates a subset of the possible paths on the unit cube.This constraint may be taken into account when using this approach togenerate a scan path on the cube.

[0401] Rings

[0402] Given the following parameterization of a ring(re-parameterization of standard parameterization)

x(u, v)=(g(u)cos(v), g(u)sin(v)) where 0<u ₀ ≦u≦u ₁ and 0≦v≦2π

[0403] Let g be sufficiently smooth, g maps [u₀,u₁] onto [u₀,u₁].According to the method described above, g(u) must satisfy an ordinarydifferential equation (α₁,α₂>0)${g^{\prime}(u)} = {{\frac{\alpha_{2}{g(u)}}{\sqrt{{c\quad {g(u)}^{2}\alpha_{1}\alpha_{2}} - \alpha_{1}^{2}}}\quad {where}\quad {g( u_{0} )}} = {{u_{0}\quad {and}\quad {g( u_{1} )}} = u_{1}}}$

[0404]FIG. 15B shows a resulting low-discrepancy curve filling the givenring for a specific case where u₁=1 and u₂=2. The parameters α₁, α₂, andc were chosen appropriately.

[0405] Surface of a Torus

[0406] The third example deals with the surface of a torus. Given the R³embedding of a torus (it is b<a)

x(u, v)=((a+b cos(2πg(v)))cos(2πu),(a+b cos(2πg(v)))sin(2πu),bsin(2πg(v)))

ds ²=4π²(a+b cos(2πv))² du ²+4π² b ² g′(v)² dv ²

dA ²=16π⁴ b ²(a+b cos(2πv))¹ g′(v)²

[0407] The function g maps [0,1] onto [0,1] and is sufficiently smooth.A constant ratio of ds and dA in α-direction can be achieved if thefollowing equation holds true (c is a constant, α₁>0): $\begin{matrix}{{\frac{{( {a + {b\quad {\cos ( {2\pi \quad {g(v)}} )}}} )^{2}\alpha_{1}^{2}} + {b^{2}{g^{\prime}(v)}^{2}\alpha_{2}^{2}}}{4\pi^{2}b^{2}{g^{\prime}(v)}^{2}( {a + {b\quad {\cos ( {2\pi \quad {g(v)}} )}}} )^{2}\alpha_{1}\alpha_{2}} = c},{{i.e.{g^{\prime}(v)}} = \frac{( {a + {b\quad {\cos ( {2\pi \quad {g(v)}} )}}} )\alpha_{1}}{\sqrt{{4c\quad {\pi^{2}( {a + {b\quad {\cos ( {2\pi \quad {g(v)}} )}}} )}^{2}\alpha_{1}\alpha_{2}} - \alpha_{2}^{2}}}}} & (26)\end{matrix}$

[0408] The boundary conditions are g(0)=0 and g(1)=1. A solution of (26)guarantees g′(0)=g′(1). The parameter c can be chosen with the aid of ashooting method. FIG. 15C shows a valid function g when a=2, b=1. FIG.15D depicts part of the resulting low-discrepancy curve lying on thesurface of a torus. Because g′(0)=g′(1) the curve is smooth.

[0409]FIG. 16—Scanning Part of a Sphere

[0410] A more sophisticated example is a part of a sphere given as anabstract surface by

ds ²=4π² sin²(πg(v))du ²+π² dv ² where (u,v) is located in [0,1]×[v ₁ ,v₁] with v ₀ <v ₁ in ]0,1[.

[0411] A Euclidean embedding of this surface is given by:

x(u, v)=(sin(2πu)sin(πg(v)), cos(2πu)sin(πg(v)), cos(πg(v)))

[0412] The function g(v) is smooth and maps [v₀,v₁] onto [v₀,v₁].According to Definition 1 and (15):${g^{\prime}(v)} = \frac{2{\sin ( {\pi \quad {g(v)}} )}}{\sqrt{{4c\quad {\pi^{2}( {\pi \quad {g(v)}} )}} - \alpha^{2}}}$

[0413] The boundary conditions are g(v₀)=v₀ and g(v₁)=v₁. FIG. 16depicts the resulting low-discrepancy curve.

[0414] Definition 2 can be used to construct low-discrepancy curves inparts of a sphere. Depending on the chosen re-parameterizationsdifferent space filling schemes can be generated. Appropriatere-parameterizations of the radius generate low-discrepancy curvesfilling a shell. Those of angular components generate space fillingcurves according to the pattern demonstrated with reference to FIG. 16.Many more re-parameterizations are possible. The concrete choice dependson the problem to be solved.

[0415] Another generalization of 2d strategies deals with smooth curvesthat scan n-dimensional spheres completely where the appropriatelydefined (absolute) curvature is always below a certain number.

[0416] However, in higher dimensional spaces there are many moreparameterizable structures than space curves. The most straightforwardgeneralization is a surface in 3d that scans an open 3-dimensionalregion completely. If curvature constraints are an issue the specificmeaning of this term must be defined more accurately. Smooth surfaces in3d are characterized by principal curvatures at any point of thesurface. These principal curvatures are orthogonal to each other. TheGaussian or mean curvatures are scalars that can be used as validdefinitions of curvature constraints. In higher-dimensional spaces theseconcepts may be even more complicated.

[0417] Another direction of optimal scanning strategies is based on theadditional assumption that crossings of paths are forbidden. Anapplication for this class of problems is a layout-like scanning schemewhere a visited point is marked by the moving system, e.g. by a realwire. Some scenarios in higher dimensional spaces where efficientscanning strategies offer a definite advantage are described below.

[0418]FIG. 17—Flowchart of a Method For Generating a Low DiscrepancyCurve on an Abstract Surface

[0419]FIG. 17 flowcharts one embodiment of a method for generating acurve, such as a Low Discrepancy Curve, on an abstract surface S.Specifically, an embodiment of the method is described in which a LowDiscrepancy Curve is generated on a unit square, then mapped to thesurface S, although it is also contemplated that other curves orsequences of points may be mapped in a similar manner, and that regionsother than the unit square may be used.

[0420] As FIG. 17 shows, in 1702 a parameterization of surface S may beselected. In the preferred embodiment, the parameter space for theparameterization is the unit square or a rectangle. Other suitablegeometries for the parameter space are also contemplated, includinghigher dimensional unit cubes and rectangles, among others.

[0421] In 1704, a Low Discrepancy Curve in the parameter space may beselected, e.g., according to the method described with reference to FIG.12. In one embodiment, selecting a Low Discrepancy Curve in theparameter space may comprise selecting or generating a vector or set ofirrational values (α₁,α₂) which may be used to generate the LowDiscrepancy Curve. Note that in higher dimensional embodiments, the αvector may comprise more than two terms, e.g., (α₁,α₂,α₃, . . . ,α_(m)).

[0422] Then, in 1706, a re-parameterization of S may be determined suchthat a ratio of line and area elements of S based on a Riemannian metricis constant. For example, as described above, if α=(α₁,α₂) is a givenvector (direction) in R², then according to (11) and (12) line and areaelements of S for a specific direction (du,dv)=(α₁du,α₂du) are given byequation (21):$\frac{s}{u} = \sqrt{( {{{E( {u,v} )}\alpha_{1}^{2}} + {2{F( {u,v} )}\alpha_{1}\alpha_{2}} + {{G( {u,v} )}\alpha_{2}^{2}}} )}$$\frac{A}{u^{2}} = {\sqrt{{{E( {u,v} )}{G( {u,v} )}} - ( {F( {u,v} )} )^{2}}\alpha_{1}\alpha_{2}}$

[0423] and so, the relation of equation (22) follows:$\frac{\frac{s}{u}}{\frac{A}{u^{2}}} = \frac{\sqrt{( {{{E( {u,v} )}\alpha_{1}^{2}} + {2{F( {u,v} )}\alpha_{1}\alpha_{2}} + {{G( {u,v} )}\alpha_{2}^{2}}} )}}{\sqrt{{{E( {u,v} )}{G( {u,v} )}} - ( {F( {u,v} )} )^{2}}\alpha_{1}\alpha_{2}}$

[0424] It is this ratio which may be held constant to generate there-parameterization of S.

[0425] In 1708, the generated Low Discrepancy Curve in the unit squaregenerated in 1704 may be mapped onto the surface S using there-parameterization of 1706.

[0426] Finally, as indicated in 1710, the mapped Low Discrepancy Curveof 1708 may be output. In one embodiment, outputting the mapped LowDiscrepancy Curve may comprise storing the curve for later use. Inanother embodiment, outputting the mapped Low Discrepancy Curve maycomprise displaying the curve on a display device.

[0427] Thus, by using the above-described method, a Low DiscrepancyCurve generated on a unit square (or other suitable geometry) may bemapped to an abstract surface. It should be noted that any LDS or LDCgenerated on the unit square (or other suitable geometry) may be mappedin this way. In other words, it is not required that the sequence orcurve be generated in the manner described in reference to FIG. 12.

[0428] In one embodiment, the above embodiment may be used to generate ascan path for a six degree of freedom alignment problem, as describedbelow in the section titled Applications.

[0429] Induced Paths in Large Point Sets

[0430] Given a large set of discrete points in a manifold. Assume awell-balanced path connecting points of this manifold has to bedetermined. Such a path should treat all parts of the point set equallywell. More importantly, the path should be well distributed in the sensethat the same amount of path length covers approximately the same numberof points of the underlying set. The following algorithm can be applied:

[0431] Algorithm II:

[0432] (II.1) Apply Theorem 4-6 to generate a low-discrepancy curve inthis manifold.

[0433] (II.2) Determine nodes and edges of the graph that minimize thedistance to this path of the manifold. To this end, appropriatethresholds must be given.

[0434] If the length of the path is unknown in advance the algorithm canstop whenever a certain goal is achieved, e.g. any point of theunderlying point set is in a neighborhood of the path constructed sofar.

[0435] Thus, many of the definitions and results described above can begeneralized to higher dimensional spaces. Smooth curves that scan openregions in n-dimensional spaces with and without curvature restrictionscan be defined as before. As mentioned, the term “curvature” must bedefined appropriately, e.g. space curves are characterized by“classical” curvature and torsion. It is also clear that low-discrepancycurves in n-dimensional spaces can be constructed where Halton sequencesbased on different prime numbers are used for the different axes. Suchschemes scan n-cubes in an efficient manner, i.e. on average they findsmall objects in the cube faster than other classical strategies.

[0436] One sophisticated scenario uses a robot arm with six degrees offreedom, such as shown in FIG. 3B, where the goal is to scan the givensurface and to guarantee a perpendicular scanning behavior, i.e. thesensor points in a normal direction of the surface. There are manypossible reasons for such a goal, e.g. the robot arm moves an ultrasonicor eddy-current probe that must be applied in an orthogonal manner toproduce valid results. The robot arm has six degrees of freedom, andtherefore acts in a 6-dimensional space, and must be moved smoothly toexplore a given situation in 3d.

[0437] The latter example gives rise to the notion of an inducedscanning scheme. Via inverse kinematics the optimal scanning strategy ofthe surface can be translated into an induced version acting on acomplicated 2d manifold in the 6-dimensional phase space of the robotarm. The induced efficient curve on this 2d manifold can be computed,recorded, and played back to implement the desired highly efficientscanning scheme of a given surface.

[0438] The same principle is applicable to phased array systems (e.g.ultrasonic equipment), as described above with reference to FIG. 3C, orother synchronized equipment such as arrays of telescopes. Somephased-array systems, e.g. those used in ultrasonic testing, can involvedimensions of very high order in scanning the interior of 3d objects.The original search space might be 2- or 3-dimensional. The inducedtrajectories act in higher or even extremely high dimensional spaces.One can regard these induced curves as optimal with respect to the givenobject to be inspected.

[0439] Exploration of an Unknown Higher Dimensional Space

[0440] In some cases the geometry of an underlying space may be unknown.An example is given by a car seat which has six degrees of freedom. Inthis example, some combinations of movement are valid and others areinvalid. The goal is a description of this space in terms of validcombinations (6 coordinates each). An efficient scanning strategy suchas those described above allows a characterization of this space, whileprior art methods are generally too time-consuming.

[0441] Direction Dependent Sensing in 3d

[0442] A typical scanning problem arises when electromagnetic, acoustic,or other fields must be explored where the sensor works in adirection-selective manner and can be moved freely, e.g. the point ofmaximal strength of an acoustic field in a given room must be determinedand the microphone must be directed to take a measurement. From a purelyscanning standpoint, a 6-dimensional space must be scanned efficiently.Low-discrepancy curves in 6d will detect this point faster on averagethan other methods. Moreover, standard techniques such as grid-likescanning methods are almost unrealizable because of the unbalancedstructure of these curves.

[0443] Applications:

[0444] Testing and Exploration

[0445] A typical scenario in nondestructive testing or in searchroutines is based on well-chosen continuous paths where measurements aretaken on the fly. Usually, the search space is large, and so efficientscanning strategies are necessary.

[0446] It is assumed that efficient continuous search strategies in thesense of Theorems 4-6 for a d-dimensional cube are available. Asdemonstrated above, continuous scanning strategies in other spaces canbe derived.

[0447]FIG. 18—Scanning of Surfaces in 3d

[0448]FIG. 18 illustrates a surface scan in 3d. Assume a surface in 3d,as shown in FIG. 18. Furthermore, assume definitions of Low DiscrepancySequences and/or Low Discrepancy Curves, e.g. based on a Riemannianmetric of the surface. In practical applications the surface structuremight represent a fuselage of a car or other smooth objects (evennon-smooth objects can be regarded as valid surfaces, e.g. surface of acube). An x-y-z stage can scan the surface where a constant height overthe surface is guaranteed. In other words, the x-y scan may be based onan efficient Low Discrepancy Curve defined on the surface, with zdependent on the curve such that constant height is maintained. As FIG.18 shows, the surface has well-defined normals at each point on thesurface. The scanning system may be operable to measure at each point onthe surface such that the scanning sensor is aligned with the normal atthat point.

[0449] Optical Fiber Alignment

[0450] As mentioned previously, scanning strategies form an essentialpart of many practical applications. The scanning schemes presentedabove are particularly suitable for a specific alignment problem infiber optics. Specifically, two arrays of optical fibers must be alignedaccurately. Typically, lasers are applied and the intensity of thesebeams can be measured to align the arrays. If the first array isregarded as fixed with unknown position and orientation, the secondarray can be moved to establish the alignment. A simple model of thisscenario consists of 3 position degrees of freedom (x, y, and z) incombination with 3 orientation degrees of freedom (pitch α, roll β andyaw angle γ). This results in a 6-dimensional search space where furtherconstraints add complexity to this space. For example,

max(|x|,|y|,|z|)≦P and max(|α−α₀|,|β−β₀|,|γ−γ₀|)≦O  (27)

[0451] where P(osition), O(rientation), α₀, β₀, and γ₀ are knownparameters. The generated space (27) may (after normalization) beinterpreted as a unit cube in 6d. Theorem 6 allows the construction oflow-discrepancy curves in 6-dimensional space. The resulting trajectoryscans (27) in an efficient manner. Note that the trajectories arecomposed of straight lines which minimizes motion control relatedproblems.

[0452] A slightly different approach may also be applied to thealignment problem. Referring again to FIG. 3D, the goal is a fastprocedure for alignment of the two fibers 320 where the intensity of ana laser beam 312 is constantly measured. The second fiber 320B can bemoved by a motion control stage to find the position where the intensityof the measured laser beam 312 is maximal. The beam 312 has aGaussian-like intensity shape and its dimensions are often very smallcompared to the original scanning region. In these cases the alignmentprocedure can be divided into two phases, a coarse search for the firstappearance of measured intensities beyond a given noise level and thefinal determination of real peaks of this intensity profile.

[0453] As part of the fiber optics alignment task, efficient scanningand optimization algorithms are required. As mentioned above, the secondfiber 320B can be moved by a motion control stage 330 to find theposition where the intensity of the measured laser beam 312 is maximal.The problem can be reformulated as a scanning task, where a small objectin a given 2d region must be identified. Usually, the underlying spaceis rather simple. Squares, rectangles, and circles form the majority ofvalid regions in practical applications.

[0454] In the first phase an efficient scanning strategy minimizes theexpected search time to locate the approximate position of the beam 312.The second phase consists of a finer scanning strategy capable oflocating the beam 312 precisely.

[0455] In one embodiment, this procedure may be performed as follows:

[0456] In the first phase, the fiber 320B which is connected to thestage 330 is moved according to a first scanning path, and the intensityof the laser beam 312 is read (e.g., using a data acquisition card). Theposition of the stage 330 at the different measured beam intensities isalso recorded. The stage 330 is then moved to align the second fiber320B with the point of maximum beam intensity.

[0457] In the second phase, the fiber 320B (via the movement stage 330)is moved according to a second scanning path, and the intensity of thelaser beam 312 is read and positions recorded. Again, the position ofmaximum beam intensity is determined, and the second fiber 320B ispositioned accordingly, thereby aligning the two fibers 320.

[0458] It should be noted that the first scanning path coverssubstantially the entire region, and is intended to provide an initial,coarse solution, while the second scanning path covers a sub-region(i.e., has a smaller window) with higher resolution coverage. Thus, thefirst phase of the procedure narrows the scope of the search to a regionof interest, and the second phase implements a high resolution finalapproach to the target, i.e., the beam center.

[0459] Typically, the search for the first appearance of valid intensityvalues is based on grid-like or spiral-like scanning strategies. Betterprocedures are desirable to further reduce the time to find the objectunder consideration. The standard method for the final approach is thehill-climbing algorithm, which is well known in the art. During theexecution of the hill-climbing algorithm the motion control stage movesin specific directions and looks for the maximum intensity on lines.Based on this maximum new lines are determined and the search startsagain. The algorithm stops when the current maximum cannot be improved.

[0460] The approaches presented above were used to implement novelscanning strategies for both the first search and the final approach. Inparticular, the following routines were implemented:

[0461] 1) Coarse search based on standard schemes (Archimedes spiral andboustrophedon path);

[0462] 2) Coarse search based on conformal spirals;

[0463] 3) Coarse search based on Low Discrepancy Sequences inconjunction with TSP;

[0464] 4) Coarse search based on Low Discrepancy Curves; and

[0465] 5) Coarse and final search based on curves depicted in FIG. 18below, in connection with fitting strategies to compute the expectedposition of peaks.

[0466] The concept behind the implementation is that a coarse search canroughly determine the location of the maximum intensity spot. A coarsesearch should be smooth (minimizing curvature) and space filling. Once aregion with a spot is hit, a region of interest (based on the Gaussiannature of the spot) can be defined and a fine search based on specificsuitable curves performed. A fitting procedure can then be applied tothe resulting (X, Y, Intensity) triplets from the fine final search.Based on the fitting procedure an analytical location for the peak canbe determined.

[0467] The main advantage of the proposed approach is that it presents asystematic procedure to define the peak of the spot. The empiricalapproach can suffer if enough noise is present to disturb the hillclimbing procedure or if the peak of the spot is a flat region, due tosensor reading errors. Another express advantage is that the searchtimes are minimized. Further details of the proposed approach arepresented below with reference to FIGS. 18-22.

[0468]FIG. 19—Splined Low Discrepancy Curve Coarse Search With RefinedFinal Approach

[0469]FIG. 19 illustrates a scanning scheme based on an initial coarsesearch, followed by a refined final approach search based on thescanning schemes of FIGS. 10B and 5A, respectively. As FIG. 19 shows,the coarse search may be performed using a Splined Low Discrepancy Curvescan path. In one embodiment, once the coarse search locates anapproximate location of an area of interest, a refined search, or finalapproach, may be performed using a continuous circle based scanningscheme (flower curve), as shown.

[0470]FIG. 20—Beam Intensity Distribution in Search Area

[0471]FIG. 20 illustrates a beam intensity field distribution in asearch area, as related to the beam location process described above. AsFIG. 20 shows, the field has a Gaussian-like distribution. To test andillustrate the scanning approach described above, an environment tosimulate the proposed continuous searching strategies was implementedusing National Instrument's software tool LabVIEW. Some assumptions weremade in the simulation: the scanning device moves at constant speed, thelight intensity is sampled at a constant frequency and the light beamhas a skewed Gaussian intensity profile in space. Furthermore, it wasassumed that the search area consisted of a unit area square, and allthe simulation variables were adjusted adequately. All the assumptionsare valid for the real world implementation of the solution. Moreover,some of the constraints such as constant speed search are desired inpractical situations. As FIG. 20 shows, for the simulation the beamradius was assumed to be 0.02, much smaller than the search area. Thenoise power was {fraction (1/20)} of the beam power.

[0472] FIGS. 21A and 21B—Location of the Peak

[0473]FIGS. 21A and 21B illustrate the location of the peak in the finalapproach search. Once a given level of light intensity is observed inthe coarse search, the system can be set to execute the final approachand estimation of the beam center. Based on the initial estimate of thebeam location, more data about the shape of the beam may be collected inthe final approach. Ideally, such data collection should allow one torecord the intensity at the exact peak. However, in practice the beammay not be an exact Gaussian, and noise may actually mask the exactlocation of the peak. Thus, a more systematic approach for the peaklocation estimation may be used, as described below.

[0474] During the coarse search, once the desired intensity threshold isobserved, the search should stop, i.e., after a lower threshold ofintensity is crossed. The local intensity profile along the curve inthis situation is represented in FIG. 21A. The (X,Y) point correspondingto the peak of the intensity profile could be taken as an initialestimate for the peak location. However, a quick line searchconsiderably improves the estimate. FIG. 21B illustrates one example ofsuch a line search. Note that the (X,Y) location corresponding to thepeak of the measured intensity on the line search is a better estimateof the peak location than the original estimate.

[0475] Based on the initial estimate of the peak location and an upperbound on the expected beam width (spot size), a data collection move canbe performed. Ideally, the move should be as smooth as possible in orderto allow an accurate tracking of the (X,Y) positions being visited bythe search. Theorem 3 and FIG. 5A present such a search path. The“flower” shaped curve composed of the union of a finite number ofcircles possesses almost constant curvature, and evenly scans thedesired area (a multiple of the upper bound on the expected beam width).

[0476] The data points collected in the final approach can then befitted using a multidimensional polynomial in order to compute anaccurate location for the intensity peak. Such an operation has theadvantage of compensating the noise present in the intensitymeasurements. The location of the peak can be computed as a function ofthe estimated polynomial coefficients. A move towards the located peakcan be done to measure the peak intensity and check the validity of theestimate.

[0477]FIG. 21C—Error Distribution of the Estimated Peak

[0478]FIG. 21C presents a distribution of the errors in the location ofthe peak for a series of 1000 experiments. As FIG. 21C shows, the errorsare distributed as a normal distribution. The main advantage of theproposed final approach is that a systematic approach for locating thepeak is developed. Moreover the approach is very robust with regard tomeasurement noise.

[0479]FIG. 22—Locating a Point of Interest in a Region

[0480]FIG. 22 is a flowchart of one embodiment of a method for locatinga point of interest in a region, where an approximate model of theregion is known. An exemplary application of this method is the opticalfiber alignment task described above with reference to FIG. 3D and underthe above section titled “Applications”.

[0481] As FIG. 22 indicates, in 1402, one or more characteristics of aregion of interest within the region may be determined, where the regionof interest includes the point of interest. In one embodiment, thedetermined characteristics of the region of interest may include theradius or boundary of the region of interest. In another embodiment, theone or more characteristics of the region of interest may comprise anapproximate location of the point of interest, e.g., a center of theregion of interest. In one embodiment, the determined characteristics ofthe region of interest may include a general topology of the region ofinterest. For example, the region of interest may comprise a datadistribution, such as a Gaussian or Gaussian-like distribution orsurface, and the point of interest may comprise an extremum (peak orlow-point) of the data distribution. In one embodiment, determining theone or more characteristics of the region of interest may compriselocating the region of interest in the region. One embodiment of amethod for locating the region of interest in the region is describedwith reference to FIG. 23, below.

[0482] In 1404, a continuous trajectory based on the one or morecharacteristics of the region of interest may be determined. In thepreferred embodiment, the continuous trajectory may allow measurement ofthe region of interest. An example of such a continuous trajectory isillustrated in FIG. 5A, described above. In one embodiment, thecontinuous trajectory may comprises a plurality of circular scan curvesegments connected by smooth transition curves, as shown in FIG. 5A. Inone embodiment, each scan curve circle may have a diameter equal to somemultiple of a determined radius of the region of interest, determined in1402 above. Thus, in one embodiment, determining the continuoustrajectory which allows sampling of the area of interest may comprisedetermining a scan path such as that described with reference to FIG.5A, above.

[0483] In 1406, the region of interest may be measured at a plurality ofpoints along the continuous trajectory to generate a sample data set.

[0484] In 1408, a surface fit of the sample data set may be performedusing the approximate model to generate a parameterized surface. Forexample, in the case that the sample data set comprises a Gaussiandistribution, the data set may be fit using a Gaussian model to generatea parameterized Gaussian surface.

[0485] In 1410, a location of the point of interest based on theparameterized surface may be calculated. For example, in the case thatthe parameterized surface comprises a parameterized Gaussian surface, aGaussian peak of the surface may be calculated using the parameterizedGaussian surface.

[0486] In 1412, the region of interest may optionally be measured at thecalculated point of interest to confirm correctness of the calculatedlocation. In other words, in one embodiment, the system may move to thecalculated location of the point of interest and make one ormeasurements to verify that the calculated location of the point ofinterest is within some specified error of the actual point of interest.

[0487] It should be noted that although the example given was for a twodimensional region and object, the method described above may be appliedto regions and objects of one or two dimensions. In other embodiments,the method may be applied to regions and objects of dimensionalitygreater than two.

[0488]FIG. 23—Locating the Region of Interest in the Region

[0489]FIG. 23 is a flowchart of one embodiment of a method for locatingthe region of interest in the region, mentioned in 1402 of FIG. 22, anddescribed with reference to FIGS. 21A and 21B, above. In one embodiment,locating the region of interest in the region may comprise locating anapproximate center of the region of interest.

[0490] As FIG. 22 indicates, in 1502, the region may be scanned tolocate two or more points of the region of interest, where each of thetwo or more points has associated measured data. In one embodiment, thetwo or more points of the region of interest may comprise an entry pointand an exit point of the region of interest.

[0491] In 1504, a first local point of interest in the region ofinterest proximate to the two or more points of the region of interestmay be determined. In one embodiment, the first local point of interestin the region of interest may be determined by scanning along a firstscan line between the two or more points of the region of interest,e.g., the entry point and the exit point. For example, the first localpoint of interest may comprise a local peak (or low-point) of a datadistribution located along the first scan line.

[0492] In 1506, a second scan line may be calculated, where the secondscan line passes through the first local point of interest, and wherethe second scan line is orthogonal to the first scan line.

[0493] In 1508, the region may be measured (scanned) along the secondscan line to generate second scan line associated measured data.

[0494] In 1510, a second local point of interest may be determined alongthe second scan line based upon the second scan line associated measureddata. For example, the second local point of interest may comprise alocal peak (or low-point) of the data distribution along the second scanline.

[0495] In 1512, the approximate center of the region of interest may bedetermined based upon one or more of the second local point of interestand the first local point of interest. In one embodiment, if the secondlocal point of interest is determined to be “less interesting” than thefirst local point of interest, e.g., if the second local point ofinterest is a lower peak than the first local point of interest, thefirst local point of interest may be selected as the determined centerof the region of interest. Alternately, if the first local point ofinterest is a lower peak than the second local point of interest, thesecond local point of interest may be selected as the determined centerof the region of interest. In one embodiment, the first and second localpoints of interest may be used to calculate a third local point ofinterest comprising the determined center of the region of interest.

[0496] In one embodiment, the method may include providing a radius,wherein the region of interest comprises an area of the region withinthe radius of the determined center. Thus, the method described abovemay locate a region of interest by locating an approximate center of theregion of interest. In one embodiment, the located approximate centerand the provided radius may be used to specify the region of interestdescribed above with reference to FIG. 22.

[0497] The scanning methodologies presented above provide efficientmethods for scanning a wide variety of geometries, including very highdimensionality spaces. In applications where object detection is underenormous real-time pressure, the methods described herein will onaverage find objects faster than other strategies necessitating acontinuous scan.

[0498] Memory and Carrier Medium

[0499] The computer system 102 preferably includes a memory medium onwhich software according to an embodiment of the present invention maybe stored. The memory medium may store one or more programs to controland execute a scanning operation. The memory medium may also store asoftware program for preprocessing scan data, such as to generate a LowDiscrepancy Sequence, described above. In one embodiment, the memorymedium may also store a software program for analyzing results of a scanoperation.

[0500] The term “memory medium” is intended to include an installationmedium, e.g., a CD-ROM, floppy disks, or tape device; a computer systemmemory or random access memory (RAM) such as DRAM, SRAM, EDO RAM, RRAM,etc.; or a non-volatile memory such as a magnetic media, e.g., a harddrive, or optical storage. The memory medium may comprise other types ofmemory as well, or combinations thereof.

[0501] In addition, the memory medium may be located in a first computerin which the software program is stored or executed, or may be locatedin a second different computer which connects to the first computer overa network, such as the Internet. In the latter instance, the secondcomputer provides the program instructions to the first computer forexecution. Also, the computer system 102 may take various forms,including a personal computer system, mainframe computer system,workstation, network appliance, Internet appliance, personal digitalassistant (PDA), television set-top box, or other device. In general,the term “computer system” can be broadly defined to encompass anydevice having at least one processor which executes instructions from amemory medium, or any device which includes programmable logic that isconfigurable to perform a method or algorithm.

[0502] Various embodiments further include receiving or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a carrier medium. Suitable carrier media include amemory medium as described above, as well as signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as networks and/or a wireless link.

[0503] However, it is noted that the present invention can be used for aplethora of applications and is not limited to the applications shownherein. In other words, the applications described herein are exemplaryonly, and the methods described herein may be used for any of variouspurposes and may be stored in and execute on any of various types ofsystems to perform any of various applications.

[0504] Although the embodiments above have been described inconsiderable detail, numerous variations and modifications will becomeapparent to those skilled in the art once the above disclosure is fullyappreciated. It is intended that the following claims be interpreted toembrace all such variations and modifications.

We claim:
 1. A method for scanning for an object within a region,comprising: scanning the region using a Low Discrepancy Curve scanningscheme; determining one or more characteristics of the object inresponse to said scanning; and generating output indicating the one ormore characteristics of the object.
 2. The method of claim 1, furthercomprising: generating a Low Discrepancy Sequence of points in theregion; and calculating a Low Discrepancy Curve in the region based onthe Low Discrepancy Sequence of points; wherein said scanning the regionusing a Low Discrepancy Curve scanning scheme comprises: measuring theregion at a plurality of points along the Low Discrepancy Curve.
 3. Themethod of claim 2, wherein said generating a Low Discrepancy Sequence ofpoints in the region comprises generating a plurality of points whereinany location in the region is within a specified distance of at leastone of the Low Discrepancy Sequence of points.
 4. The method of claim 2,wherein said calculating a Low Discrepancy Curve comprises: for eachsuccessive pair of the Low Discrepancy Sequence of points: determiningone or more orthogonal line segments which connect the pair of points;and re-sampling the one or more orthogonal line segments to generate aLow Discrepancy Curve segment; wherein the Low Discrepancy Curvecomprises a contiguous sequence of the Low Discrepancy Curve segmentsfrom the successive pairs of the Low Discrepancy Sequence of points. 5.The method of claim 4, wherein the one or more orthogonal line segmentscomprises a first sequence of points, wherein the first sequence ofpoints defines a first trajectory having a first maximum curvature;wherein said re-sampling the one or more orthogonal line segmentscomprises manipulating the first sequence of points to generate the LowDiscrepancy Curve segment; wherein the Low Discrepancy Curve segmentdefines a second trajectory having a second maximum curvature which isless than the first maximum curvature;
 6. The method of claim 4, whereinthe Low Discrepancy Curve segments corresponding to the successive pairsof the Low Discrepancy Sequence of points are sequentially connected toform the Low Discrepancy Curve.
 7. The method of claim 4, wherein theregion is defined by a coordinate space having a plurality of orthogonalaxes, wherein each of the plurality of orthogonal axes correspondsrespectively to a dimension of the region; wherein each of the pair ofpoints has a plurality of coordinates corresponding respectively to theplurality of orthogonal axes; wherein each of the one or more linesegments is parallel to a respective one of the orthogonal axes; andwherein each of the one or more line segments has a first endpoint and asecond endpoint, wherein the first endpoint has a first plurality ofcoordinates, wherein the second endpoint has a second plurality ofcoordinates, and wherein said first plurality of coordinates and saidsecond plurality of coordinates differ only in value of a coordinatecorresponding to a respective one of the plurality of orthogonal axes.8. The method of claim 7, wherein said one or more orthogonal linesegments which connect the pair of points comprises a contiguoussequence of one or more of said line segments corresponding to aspecified order of the plurality of orthogonal axes; and wherein saidre-sampling the one or more orthogonal line segments to generate a LowDiscrepancy Curve segment comprises re-sampling said contiguous sequenceof said one or more of said line segments in the specified order togenerate the Low Discrepancy Curve segment.
 9. The method of claim 8,wherein said plurality of orthogonal axes comprises an x axis and a yaxis, wherein said region has a dimensionality of two, and wherein saidone or more line segments comprises two orthogonal line segmentscomprising a first line segment and a second line segment; wherein afirst of the pair of points has two coordinates, (x0, y0), correspondingrespectively to the x and y axes; wherein a second of the pair of pointshas two coordinates, (x1, y1), corresponding respectively to the x and yaxes; wherein each of the line segments has a first endpoint and asecond endpoint, wherein the second endpoint of the first line segmentis equal to the first endpoint of the second line segment; wherein saidtwo orthogonal line segments which connect the pair of points comprise acontiguous sequence of said line segments in the specified order; andwherein said re-sampling the two orthogonal line segments to generate aLow Discrepancy Curve segment comprises re-sampling said contiguoussequence of said line segments in the specified order to generate theLow Discrepancy Curve segment.
 10. The method of claim 9, wherein thespecified order of the plurality of orthogonal axes is (x, y); whereinthe first endpoint of a first of the two line segments has coordinates(x0, y0), and the second endpoint of the first of the two line segmentshas coordinates (x1, y0); and wherein the first endpoint of a second ofthe two line segments has coordinates (x1, y0), and the second endpointof the second of the two line segments has coordinates (x1, y1).
 11. Themethod of claim 9, wherein the specified order of the plurality oforthogonal axes is (y, x); wherein the first endpoint of a first of thetwo line segments has coordinates (x0, y0), and the second endpoint ofthe first of the two line segments has coordinates (x0, y1); wherein thefirst endpoint of a second of the two line segments has coordinates (x0,y1), and the second endpoint of the second of the two line segments hascoordinates (x1, y1);
 12. The method of claim 8, wherein said pluralityof orthogonal axes comprises an x axis, a y axis, and a z axis, whereinsaid region has a dimensionality of three, and wherein said one or moreline segments comprises three orthogonal line segments comprising afirst line segment, a second line segment, and a third line segment;wherein a first of the pair of points has three coordinates, (x0, y0,z0), corresponding respectively to the x, y, and z axes; wherein asecond of the pair of points has three coordinates, (x1, y1, z1),corresponding respectively to the x, y, and z axes; wherein each of theline segments has a first endpoint and a second endpoint, wherein thesecond endpoint of the first line segment is equal to the first endpointof the second line segment, and wherein the second endpoint of thesecond line segment is equal to the first endpoint of the third linesegment; wherein said three orthogonal line segments which connect thepair of points comprise a contiguous sequence of said line segments inthe specified order; and wherein said re-sampling the three orthogonalline segments to generate a Low Discrepancy Curve segment comprisesre-sampling said contiguous sequence of said line segments in thespecified order to generate the Low Discrepancy Curve segment.
 13. Themethod of claim 12, wherein the specified order of the plurality oforthogonal axes is (x, y, z); wherein the first endpoint of a first ofthe three line segments has coordinates (x0, y0, z0) and the secondendpoint of the first of the three line segments has coordinates (x1,y0, z0); wherein the first endpoint of a second of the three linesegments has coordinates (x1, y0, z0), and the second endpoint of thesecond of the three line segments has coordinates (x1, y1, z0); andwherein the first endpoint of a third of the three line segments hascoordinates (x1, y1, z0), and the second endpoint of the third of thethree line segments has coordinates (x1, y1, z1).
 14. The method ofclaim 12, wherein the specified order of the plurality of orthogonalaxes is (x, z, y); wherein the first endpoint of a first of the threeline segments has coordinates (x0, y0, z0), and the second endpoint ofthe first of the three line segments has coordinates (x1, y0, z0);wherein the first endpoint of a second of the three line segments hascoordinates (x1, y0, z0), and the second endpoint of the second of thethree line segments has coordinates (x1, y0, z1); and wherein the firstendpoint of a third of the three line segments has coordinates (x1, y0,z1), and the second endpoint of the third of the three line segments hascoordinates (x1, y1, z1).
 15. The method of claim 12, wherein thespecified order of the plurality of orthogonal axes is (y, z, x);wherein the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x0, y1, z0); wherein the firstendpoint of a second of the three line segments has coordinates (x0, y1,z0), and the second endpoint of the second of the three line segmentshas coordinates (x0, y1, z1); and wherein the first endpoint of a thirdof the three line segments has coordinates (x0, y1, z1), and the secondendpoint of the third of the three line segments has coordinates (x1,y1, z1).
 16. The method of claim 12, wherein the specified order of theplurality of orthogonal axes is (y, x, z); wherein the first endpoint ofa first of the three line segments has coordinates (x0, y0, z0), and thesecond endpoint of the first of the three line segments has coordinates(x0, y1, z0); wherein the first endpoint of a second of the three linesegments has coordinates (x0, y1, z0), and the second endpoint of thesecond of the three line segments has coordinates (x1, y1, z0); andwherein the first endpoint of a third of the three line segments hascoordinates (x1, y1, z0), and the second endpoint of the third of thethree line segments has coordinates (x1, y1, z1).
 17. The method ofclaim 12, wherein the specified order of the plurality of orthogonalaxes is (z, x, y); wherein the first endpoint of a first of the threeline segments has coordinates (x0, y0, z0), and the second endpoint ofthe first of the three line segments has coordinates (x0, y0, z1);wherein the first endpoint of a second of the three line segments hascoordinates (x0, y0, z1), and the second endpoint of the second of thethree line segments has coordinates (x1, y0, z1); and wherein the firstendpoint of a third of the three line segments has coordinates (x1, y0,z1), and the second endpoint of the third of the three line segments hascoordinates (x1, y1, z1).
 18. The method of claim 12, wherein thespecified order of the plurality of orthogonal axes is (z, y, x);wherein the first endpoint of a first of the three line segments hascoordinates (x0, y0, z0), and the second endpoint of the first of thethree line segments has coordinates (x0, y0, z1); wherein the firstendpoint of a second of the three line segments has coordinates (x0, y0,z1), and wherein the second endpoint of the second of the three linesegments has coordinates (x0, y1, z1); and wherein the first endpoint ofa third of the three line segments has coordinates (x0, y1, z1), and thesecond endpoint of the third of the three line segments has coordinates(x1, y1, z1).
 19. The method of claim 4, wherein said re-sampling theplurality of orthogonal line segments comprises: fitting a curve to aplurality of points comprised in the plurality of orthogonal linesegments subject to one or more constraints; and generating a secondplurality of points along the fit curve, wherein said second pluralityof points define the Low Discrepancy Curve segment.
 20. The method ofclaim 2, wherein said generating a Low Discrepancy Sequence of points onthe object is performed prior to said scanning.
 21. The method of claim2, further comprising: the object entering the region prior to saidscanning the region.
 22. The method of claim 2, wherein said generatinga Low Discrepancy Sequence of points in the region and said calculatinga Low Discrepancy Curve in the region based on the Low DiscrepancySequence of points are performed in a preprocessing phase of the method.23. The method of claim 2, wherein said generating a Low DiscrepancySequence of points in the region and said calculating a Low DiscrepancyCurve in the region based on the Low Discrepancy Sequence of points areperformed offline as a preprocessing operation.
 24. The method of claim2, wherein, said measuring the region at a plurality of points along theLow Discrepancy Curve is performed in a real time phase of the method.25. The method of claim 2, wherein said generating a Low DiscrepancySequence of points on the object and said calculating a Low DiscrepancyCurve on the object based on the Low Discrepancy Sequence of points isperformed in real time.
 26. The method of claim 1, wherein the regionhas a dimensionality of one of one, two, and three.
 27. The method ofclaim 1, wherein the region has a dimensionality greater than three. 28.The method of claim 1, wherein said scanning the region using a LowDiscrepancy Curve scanning scheme comprises: a) generating a first LowDiscrepancy Sequence of points in the region; b) calculating a first LowDiscrepancy Curve segment in the region based on the first LowDiscrepancy Sequence of points; c) scanning a portion of the regionalong the first Low Discrepancy Curve segment to identify acharacteristic of the object; if the characteristic of the object is notidentified, then: d) generating a second Low Discrepancy Sequence ofpoints in the region based on previous Low Discrepancy Sequence points;e) calculating a second Low Discrepancy Curve segment in the regionbased on the second Low Discrepancy Sequence of points; f) scanning aportion of the region along the second Low Discrepancy Curve segment toidentify a characteristic of the object; g) repeating d)-f) one or moretimes until the characteristic of the object is identified or until saidone or more times equals a threshold number of times.
 29. The method ofclaim 28, wherein a) and b) are performed offline in a preprocessingphase of the method; and wherein c)-g) are performed in a real timephase of the method.
 30. The method of claim 29, wherein said second LowDiscrepancy Sequence of points includes a last point of an immediatelyprevious Low Discrepancy Curve segment and one or more additional LowDiscrepancy Sequence points.
 31. The method of claim 29, wherein c) andf) respectively comprise measuring the region at a plurality of pointsalong the first Low Discrepancy Curve; and measuring the region at aplurality of points along the second Low Discrepancy Curve.
 32. Themethod of claim 29, wherein said generating a second Low DiscrepancySequence of points in the region comprises generating a plurality ofpoints wherein any location in the region is within a specified distanceof at least one of the first Low Discrepancy Sequence of points or thesecond Low Discrepancy Sequence of points.
 33. The method of claim 29,wherein said calculating a second Low Discrepancy Curve comprises: foreach successive pair of the second Low Discrepancy Sequence of points:determining a plurality of orthogonal line segments which connect thepair of points; and re-sampling the plurality of orthogonal linesegments to generate a Low Discrepancy Curve segment; wherein, thesecond Low Discrepancy Curve comprises a contiguous sequence of the LowDiscrepancy Curve segments from the successive pairs of the second LowDiscrepancy Sequence of points.
 34. The method of claim 33, wherein theplurality of orthogonal line segments comprises a first sequence ofpoints, wherein the first sequence of points defines a first trajectoryhaving a first maximum curvature; wherein said re-sampling the pluralityof orthogonal line segments comprises manipulating the first sequence ofpoints to generate the second Low Discrepancy Curve segment; wherein thesecond Low Discrepancy Curve segment defines a second trajectory havinga second maximum curvature which is less than the first maximumcurvature;
 35. The method of claim 33, wherein the region is defined bya coordinate space having a plurality of orthogonal axes, wherein eachof the plurality of orthogonal axes corresponds respectively to adimension of the region; wherein each of the pair of points has aplurality of coordinates corresponding respectively to the plurality oforthogonal axes; wherein each of the plurality of line segments isparallel to a respective one of the orthogonal axes; wherein each of theplurality of line segments has a first endpoint and a second endpoint,wherein the first endpoint has a first plurality of coordinates, whereinthe second endpoint has a second plurality of coordinates, and whereinsaid first plurality of coordinates and said second plurality ofcoordinates differ only in value of a coordinate corresponding to arespective one of the plurality of orthogonal axes; wherein saidplurality of orthogonal line segments which connect the pair of pointscomprises a contiguous sequence of said line segments corresponding to aspecified order of the plurality of orthogonal axes; and wherein saidre-sampling the plurality of orthogonal line segments to generate a LowDiscrepancy Curve segment comprises re-sampling said contiguous sequenceof said line segments in the specified order to generate the LowDiscrepancy Curve segment.
 36. The method of claim 33, wherein saidre-sampling the plurality of orthogonal line segments comprises: fittinga curve to a plurality of points comprised in the plurality oforthogonal line segments subject to one or more constraints; andgenerating a second plurality of points along the fit curve, whereinsaid second plurality of points define the Low Discrepancy Curvesegment.
 37. The method of claim 29, wherein the region has adimensionality of one of one, two, and three.
 38. The method of claim29, wherein the region has a dimensionality greater than three.
 39. Asystem for scanning for an object within a region, comprising: a sensor;and a computer which is operable to couple to said sensor, said computercomprising: a CPU; and a memory medium which is operable to store ascanning program; wherein said CPU is operable to execute said scanningprogram to perform: scanning the region using a Low Discrepancy Curvescanning scheme; determining one or more characteristics of the objectin response to said scanning; and generating output indicating the oneor more characteristics of the object.
 40. The system of claim 39,wherein said CPU is further operable to execute said scanning program toperform: generating a Low Discrepancy Sequence of points in the region;and calculating a Low Discrepancy Curve in the region based on the LowDiscrepancy Sequence of points; wherein said scanning the region using aLow Discrepancy Curve scanning scheme comprises: measuring the region ata plurality of points along the Low Discrepancy Curve.
 41. The system ofclaim 40, wherein said generating a Low Discrepancy Sequence of pointsin the region comprises generating a plurality of points wherein anylocation in the region is within a specified distance of at least one ofthe Low Discrepancy Sequence of points.
 42. The system of claim 41,wherein said calculating a Low Discrepancy Curve comprises: for eachsuccessive pair of the Low Discrepancy Sequence of points: determining aplurality of orthogonal line segments which connect the pair of points;and re-sampling the plurality of orthogonal line segments to generate aLow Discrepancy Curve segment; wherein, the Low Discrepancy Curvecomprises a contiguous sequence of the Low Discrepancy Curve segmentsfrom the successive pairs of the Low Discrepancy Sequence of points. 43.The system of claim 42, wherein the plurality of orthogonal linesegments comprises a first sequence of points, wherein the firstsequence of points defines a first trajectory having a first maximumcurvature; wherein said re-sampling the plurality of orthogonal linesegments comprises manipulating the first sequence of points to generatethe Low Discrepancy Curve segment; wherein the Low Discrepancy Curvesegment defines a second trajectory having a second maximum curvaturewhich is less than the first maximum curvature;
 44. The system of claim42, wherein the Low Discrepancy Curve segments corresponding to thesuccessive pairs of the Low Discrepancy Sequence of points aresequentially connected to form the Low Discrepancy Curve.
 45. The systemof claim 42, wherein the region is defined by a coordinate space havinga plurality of orthogonal axes, wherein each of the plurality oforthogonal axes corresponds respectively to a dimension of the region;wherein each of the pair of points has a plurality of coordinatescorresponding respectively to the plurality of orthogonal axes; whereineach of the plurality of line segments is parallel to a respective oneof the orthogonal axes; wherein each of the plurality of line segmentshas a first endpoint and a second endpoint, wherein the first endpointhas a first plurality of coordinates, wherein the second endpoint has asecond plurality of coordinates, and wherein said first plurality ofcoordinates and said second plurality of coordinates differ only invalue of a coordinate corresponding to a respective one of the pluralityof orthogonal axes.
 46. The system of claim 45, wherein said pluralityof orthogonal line segments which connect the pair of points comprises acontiguous sequence of said line segments corresponding to a specifiedorder of the plurality of orthogonal axes; and wherein said re-samplingthe plurality of orthogonal line segments to generate a Low DiscrepancyCurve segment comprises re-sampling said contiguous sequence of saidline segments in the specified order to generate the Low DiscrepancyCurve segment.
 47. The system of claim 42, wherein said re-sampling theplurality of orthogonal line segments comprises: fitting a curve to aplurality of points comprised in the plurality of orthogonal linesegments subject to one or more constraints; and generating a secondplurality of points along the fit curve, wherein said second pluralityof points define the Low Discrepancy Curve segment.
 48. The system ofclaim 40, wherein said generating a Low Discrepancy Sequence of pointson the object is performed prior to said scanning.
 49. The system ofclaim 40, further comprising: wherein said generating a Low DiscrepancySequence of points in the region and said calculating a Low DiscrepancyCurve in the region based on the Low Discrepancy Sequence of points areperformed in a preprocessing phase.
 50. The system of claim 40, whereinsaid generating a Low Discrepancy Sequence of points on the object andsaid calculating a Low Discrepancy Curve on the object based on the LowDiscrepancy Sequence of points is performed in real time.
 51. The systemof claim 39, wherein the region has a dimensionality of one of one, two,and three.
 52. The system of claim 39, wherein the region has adimensionality greater than three.
 53. The system of claim 39, whereinsaid scanning the region using a Low Discrepancy Curve scanning schemecomprises: a) generating a first Low Discrepancy Sequence of points inthe region; b) calculating a first Low Discrepancy Curve segment in theregion based on the first Low Discrepancy Sequence of points; c)scanning a portion of the region along the first Low Discrepancy Curvesegment to identify a characteristic of the object; if thecharacteristic of the object is not identified, then: d) generating asecond Low Discrepancy Sequence of points in the region based onprevious Low Discrepancy Sequence points; e) calculating a second LowDiscrepancy Curve segment in the region based on the second LowDiscrepancy Sequence of points; f) scanning a portion of the regionalong the second Low Discrepancy Curve segment to identify acharacteristic of the object; g) repeating d)-f) one or more times untilthe characteristic of the object is identified or until said one or moretimes equals a threshold number of times.
 54. The system of claim 53,wherein a) and b) are performed offline in a preprocessing phase; andwherein c)-g) are performed in a real time phase.
 55. The system ofclaim 54, wherein said second Low Discrepancy Sequence of pointsincludes a last point of an immediately previous Low Discrepancy Curvesegment and one or more additional Low Discrepancy Sequence points. 56.The system of claim 53, wherein said generating a second Low DiscrepancySequence of points in the region comprises generating a plurality ofpoints wherein any location in the region is within a specified distanceof at least one of the first Low Discrepancy Sequence of points or thesecond Low Discrepancy Sequence of points.
 57. The system of claim 53,wherein said calculating a second Low Discrepancy Curve comprises: foreach successive pair of the second Low Discrepancy Sequence of points:determining a plurality of orthogonal line segments which connect thepair of points; and re-sampling the plurality of orthogonal linesegments to generate a Low Discrepancy Curve segment; wherein, thesecond Low Discrepancy Curve comprises a contiguous sequence of the LowDiscrepancy Curve segments from the successive pairs of the second LowDiscrepancy Sequence of points.
 58. The system of claim 57, wherein theplurality of orthogonal line segments comprises a first sequence ofpoints, wherein the first sequence of points defines a first trajectoryhaving a first maximum curvature; wherein said re-sampling the pluralityof orthogonal line segments comprises manipulating the first sequence ofpoints to generate the second Low Discrepancy Curve segment; wherein thesecond Low Discrepancy Curve segment defines a second trajectory havinga second maximum curvature which is less than the first maximumcurvature;
 59. The system of claim 56, wherein said re-sampling theplurality of orthogonal line segments comprises: fitting a curve to aplurality of points comprised in the plurality of orthogonal linesegments subject to one or more constraints; and generating a secondplurality of points along the fit curve, wherein said second pluralityof points define the Low Discrepancy Curve segment.
 60. The system ofclaim 53, wherein the region has a dimensionality of one of one, two,and three.
 61. A memory medium containing program instructions which areexecutable to scan for an object within a region, wherein said programinstructions are executable to perform: scanning the region using a LowDiscrepancy Curve scanning scheme; determining one or morecharacteristics of the object in response to said scanning; andgenerating output indicating the one or more characteristics of theobject.
 62. The memory medium of claim 61, wherein said programinstructions are further executable to perform: generating a LowDiscrepancy Sequence of points in the region; and calculating a LowDiscrepancy Curve in the region based on the Low Discrepancy Sequence ofpoints; wherein said scanning the region using a Low Discrepancy Curvescanning scheme comprises: measuring the region at a plurality of pointsalong the Low Discrepancy Curve.
 63. The memory medium of claim 62,wherein said generating a Low Discrepancy Sequence of points in theregion comprises generating a plurality of points wherein any locationin the region is within a specified distance of at least one of the LowDiscrepancy Sequence of points.
 64. The memory medium of claim 62,wherein said calculating a Low Discrepancy Curve comprises: for eachsuccessive pair of the Low Discrepancy Sequence of points: determiningone or more orthogonal line segments which connect the pair of points;and re-sampling the one or more orthogonal line segments to generate aLow Discrepancy Curve segment; wherein the Low Discrepancy Curvecomprises a contiguous sequence of the Low Discrepancy Curve segmentsfrom the successive pairs of the Low Discrepancy Sequence of points. 65.The memory medium of claim 64, wherein the one or more orthogonal linesegments comprises a first sequence of points, wherein the firstsequence of points defines a first trajectory having a first maximumcurvature; wherein said re-sampling the one or more orthogonal linesegments comprises manipulating the first sequence of points to generatethe Low Discrepancy Curve segment; wherein the Low Discrepancy Curvesegment defines a second trajectory having a second maximum curvaturewhich is less than the first maximum curvature;
 66. The memory medium ofclaim 64, wherein the Low Discrepancy Curve segments corresponding tothe successive pairs of the Low Discrepancy Sequence of points aresequentially connected to form the Low Discrepancy Curve.
 67. The memorymedium of claim 64, wherein the region is defined by a coordinate spacehaving a plurality of orthogonal axes, wherein each of the plurality oforthogonal axes corresponds respectively to a dimension of the region;wherein each of the pair of points has a plurality of coordinatescorresponding respectively to the plurality of orthogonal axes; whereineach of the one or more line segments is parallel to a respective one ofthe orthogonal axes; and wherein each of the one or more line segmentshas a first endpoint and a second endpoint, wherein the first endpointhas a first plurality of coordinates, wherein the second endpoint has asecond plurality of coordinates, and wherein said first plurality ofcoordinates and said second plurality of coordinates differ only invalue of a coordinate corresponding to a respective one of the pluralityof orthogonal axes.
 68. The memory medium of claim 67, wherein said oneor more orthogonal line segments which connect the pair of pointscomprises a contiguous sequence of one or more of said line segmentscorresponding to a specified order of the plurality of orthogonal axes;and wherein said re-sampling the one or more orthogonal line segments togenerate a Low Discrepancy Curve segment comprises re-sampling saidcontiguous sequence of said one or more of said line segments in thespecified order to generate the Low Discrepancy Curve segment.
 69. Thememory medium of claim 68, wherein said plurality of orthogonal axescomprises an x axis and a y axis, wherein said region has adimensionality of two, and wherein said one or more line segmentscomprises two orthogonal line segments comprising a first line segmentand a second line segment; wherein a first of the pair of points has twocoordinates, (x0, y0), corresponding respectively to the x and y axes;wherein a second of the pair of points has two coordinates, (x1, y1),corresponding respectively to the x and y axes; wherein each of the linesegments has a first endpoint and a second endpoint, wherein the secondendpoint of the first line segment is equal to the first endpoint of thesecond line segment; wherein said two orthogonal line segments whichconnect the pair of points comprise a contiguous sequence of said linesegments in the specified order; and wherein said re-sampling the twoorthogonal line segments to generate a Low Discrepancy Curve segmentcomprises re-sampling said contiguous sequence of said line segments inthe specified order to generate the Low Discrepancy Curve segment. 70.The memory medium of claim 68, wherein said plurality of orthogonal axescomprises an x axis, a y axis, and a z axis, wherein said region has adimensionality of three, and wherein said one or more line segmentscomprises three orthogonal line segments comprising a first linesegment, a second line segment, and a third line segment; wherein afirst of the pair of points has three coordinates, (x0, y0, z0),corresponding respectively to the x, y, and z axes; wherein a second ofthe pair of points has three coordinates, (x1, y1, z1), correspondingrespectively to the x, y, and z axes; wherein each of the line segmentshas a first endpoint and a second endpoint, wherein the second endpointof the first line segment is equal to the first endpoint of the secondline segment, and wherein the second endpoint of the second line segmentis equal to the first endpoint of the third line segment; wherein saidthree orthogonal line segments which connect the pair of points comprisea contiguous sequence of said line segments in the specified order; andwherein said re-sampling the three orthogonal line segments to generatea Low Discrepancy Curve segment comprises re-sampling said contiguoussequence of said line segments in the specified order to generate theLow Discrepancy Curve segment.
 71. The memory medium of claim 64,wherein said re-sampling the plurality of orthogonal line segmentscomprises: fitting a curve to a plurality of points comprised in theplurality of orthogonal line segments subject to one or moreconstraints; and generating a second plurality of points along the fitcurve, wherein said second plurality of points define the LowDiscrepancy Curve segment.
 72. The memory medium of claim 62, whereinsaid generating a Low Discrepancy Sequence of points on the object isperformed prior to said scanning.
 73. The memory medium of claim 62,wherein said generating a Low Discrepancy Sequence of points on theobject is performed during said scanning.
 74. The memory medium of claim62, wherein said generating a Low Discrepancy Sequence of points in theregion and said calculating a Low Discrepancy Curve in the region basedon the Low Discrepancy Sequence of points are performed offline in apreprocessing phase.
 75. The memory medium of claim 62, wherein saidgenerating a Low Discrepancy Sequence of points in the region and saidcalculating a Low Discrepancy Curve in the region based on the LowDiscrepancy Sequence of points are performed offline as a preprocessingoperation.
 76. The memory medium of claim 62, wherein, said measuringthe region at a plurality of points along the Low Discrepancy Curve isperformed online in a real time phase.
 77. The memory medium of claim62, wherein said generating a Low Discrepancy Sequence of points on theobject and said calculating a Low Discrepancy Curve on the object basedon the Low Discrepancy Sequence of points is performed in real time. 78.The memory medium of claim 61, wherein the region has a dimensionalityof one of one, two, and three.
 79. The memory medium of claim 61,wherein the region has a dimensionality greater than three.
 80. Thememory medium of claim 61, wherein said scanning the region using a LowDiscrepancy Curve scanning scheme comprises: a) generating a first LowDiscrepancy Sequence of points in the region; b) calculating a first LowDiscrepancy Curve segment in the region based on the first LowDiscrepancy Sequence of points; c) scanning a portion of the regionalong the first Low Discrepancy Curve segment to identify acharacteristic of the object; if the characteristic of the object is notidentified, then: d) generating a second Low Discrepancy Sequence ofpoints in the region based on previous Low Discrepancy Sequence points;e) calculating a second Low Discrepancy Curve segment in the regionbased on the second Low Discrepancy Sequence of points; f) scanning aportion of the region along the second Low Discrepancy Curve segment toidentify a characteristic of the object; g) repeating d)-f) one or moretimes until the characteristic of the object is identified or until saidone or more times equals a threshold number of times.
 81. The memorymedium of claim 80, wherein a) and b) are performed offline in apreprocessing phase; and wherein c)-g) are performed online in a realtime phase.
 82. The memory medium of claim 81, wherein said second LowDiscrepancy Sequence of points includes a last point of an immediatelyprevious Low Discrepancy Curve segment and one or more additional LowDiscrepancy Sequence points.
 83. The memory medium of claim 81, whereinc) and f) respectively comprise measuring the region at a plurality ofpoints along the first Low Discrepancy Curve; and measuring the regionat a plurality of points along the second Low Discrepancy Curve.
 84. Thememory medium of claim 81, wherein said generating a second LowDiscrepancy Sequence of points in the region comprises generating aplurality of points wherein any location in the region is within aspecified distance of at least one of the first Low Discrepancy Sequenceof points or the second Low Discrepancy Sequence of points.
 85. Thememory medium of claim 81, wherein said calculating a second LowDiscrepancy Curve comprises: for each successive pair of the second LowDiscrepancy Sequence of points: determining a plurality of orthogonalline segments which connect the pair of points; and re-sampling theplurality of orthogonal line segments to generate a Low DiscrepancyCurve segment; wherein, the second Low Discrepancy Curve comprises acontiguous sequence of the Low Discrepancy Curve segments from thesuccessive pairs of the second Low Discrepancy Sequence of points. 86.The memory medium of claim 85, wherein the plurality of orthogonal linesegments comprises a first sequence of points, wherein the firstsequence of points defines a first trajectory having a first maximumcurvature; wherein said re-sampling the plurality of orthogonal linesegments comprises manipulating the first sequence of points to generatethe second Low Discrepancy Curve segment; wherein the second LowDiscrepancy Curve segment defines a second trajectory having a secondmaximum curvature which is less than the first maximum curvature; 87.The memory medium of claim 85, wherein the region is defined by acoordinate space having a plurality of orthogonal axes, wherein each ofthe plurality of orthogonal axes corresponds respectively to a dimensionof the region; wherein each of the pair of points has a plurality ofcoordinates corresponding respectively to the plurality of orthogonalaxes; wherein each of the plurality of line segments is parallel to arespective one of the orthogonal axes; wherein each of the plurality ofline segments has a first endpoint and a second endpoint, wherein thefirst endpoint has a first plurality of coordinates, wherein the secondendpoint has a second plurality of coordinates, and wherein said firstplurality of coordinates and said second plurality of coordinates differonly in value of a coordinate corresponding to a respective one of theplurality of orthogonal axes; wherein said plurality of orthogonal linesegments which connect the pair of points comprises a contiguoussequence of said line segments corresponding to a specified order of theplurality of orthogonal axes; and wherein said re-sampling the pluralityof orthogonal line segments to generate a Low Discrepancy Curve segmentcomprises re-sampling said contiguous sequence of said line segments inthe specified order to generate the Low Discrepancy Curve segment. 88.The memory medium of claim 85, wherein said re-sampling the plurality oforthogonal line segments comprises: fitting a curve to a plurality ofpoints comprised in the plurality of orthogonal line segments subject toone or more constraints; and generating a second plurality of pointsalong the fit curve, wherein said second plurality of points define theLow Discrepancy Curve segment.
 89. The memory medium of claim 81,wherein the region has a dimensionality of one of one, two, and three.90. The memory medium of claim 81, wherein the region has adimensionality greater than three.
 91. A method for scanning for anobject within a region, comprising: generating a Low DiscrepancySequence of points in the region; and calculating a curve in the regionbased on the Low Discrepancy Sequence of points; scanning the regionusing the curve; determining one or more characteristics of the objectin response to said scanning; and generating output indicating the oneor more characteristics of the object.
 92. The method of claim 9 1,wherein said calculating the curve in the region based on the LowDiscrepancy Sequence of points comprises calculating the curve such thatthe curve is substantially proximate to the Low Discrepancy Sequence ofpoints in the region.
 93. The method of claim 91, wherein saidcalculating the curve in the region based on the Low DiscrepancySequence of points comprises calculating the curve such that the curvesubstantially passes through the Low Discrepancy Sequence of points inthe region.
 94. The method of claim 91, wherein said calculating a curvecomprises: for each successive pair of the Low Discrepancy Sequence ofpoints: determining one or more orthogonal line segments which connectthe pair of points; and re-sampling the one or more orthogonal linesegments to generate a curve segment; wherein the curve comprises acontiguous sequence of the curve segments from the successive pairs ofthe Low Discrepancy Sequence of points.
 95. The method of claim 94,wherein the one or more orthogonal line segments comprises a firstsequence of points, wherein the first sequence of points defines a firsttrajectory having a first maximum curvature; wherein said re-samplingthe one or more orthogonal line segments comprises manipulating thefirst sequence of points to generate the curve segment; wherein thecurve segment defines a second trajectory having a second maximumcurvature which is less than the first maximum curvature;
 96. The methodof claim 94, wherein the curve segments corresponding to the successivepairs of the Low Discrepancy Sequence of points are sequentiallyconnected to form the curve.